text:
Russia >
Russian Revolution >
Raymond Robins
Raymond Robins, the son of Charles Ephraim Robins (1832–1893) and Hannah Maria Crow (1836–1901), was born on Staten Island on 17th September, 1873. His mother was an opera singer, was committed to an insane asylum when he was a child.
Charles Robins was an insurance broker and banker. He was also a follower of Robert Owen and held progressive political views. Raymond and his sister, Elizabeth Robins, grew up with radical political opinions. Elizabeth became an actress and in 1888 she travelled to London where she introduced British audiences to the work of Henrik Ibsen. Over the next few years Elizabeth Robins was one of the most popular actresses on the West End stage.
Robins was a rebel as a young man he worked as a coal miner in Tennessee and Colorado. He returned to education and graduated from George Washington University in 1896. Robins took part in the Klondike Gold Rush in 1897. The following year he converted to Christianity, and became pastor for a Congregational church in Nome, Alaska.
Robins was deeply influenced by Christian Socialism and in 1900 he moved to Chicago where became involved in the Hull House movement. Established by Jane Addams and Ellen Starr ten years earlier, members of the community included Florence Kelley, Edith Abbott, Grace Abbott, Alice Hamilton, Charlotte Perkins, William Walling, Charles Beard, Mary McDowell, Mary Kenney, Alzina Stevens and Sophonisba Breckinridge.
Addams recruited university teachers, students and social reformers to provide free lectures on a wide variety of different topics. Over the years this included people such as Robins, John Dewey, Clarence Darrow, Susan B. Anthony, William Walling, Robert Hunter, Robert Lovett, Ernest Moore, Charles Beard, Paul Kellogg, Jenkin Lloyd Jones, Ray Stannard Baker, Francis Hackett, Henry Demarest Lloyd and Frank Lloyd Wright.
In 1905 Margaret Dreier heard Raymond Robins deliver a lecture on the social gospel in a church in Brooklyn. Soon afterwards the couple got married. Robins was a member of the Chicago Board of Education from 1906 to 1909. He served also as social service expert for the Men and Religion Forward Movement. He was leader of the National Christian Social Evangelistic campaign in 1915.
Raymond Robins in about 1915
William Hard was a close friend and he later recalled that he turned away from the socialism of his youth: "He was living then at a settlement in Chicago called the Chicago Commons. It had an open forum called, I think I remember, the Free Floor. On that Free Floor, and in little halls on North Clark Street, and in all sorts of other places, Robins and I heard all sorts of Bolshevik oratory in the Chicago of the early innocent twentieth century.... Raymond Robins' favorite intellectual pursuit was to go after it with all the arguments he could think of. In forums and in halls and at that earnest gathering of local truth-seekers called the Friday Lunch Club he never missed an opportunity to argue against Bolshevism and against every other form of Socialism."
Robins became a leading figure in the Progressive Party and served as chairman of the State Central Committee. Theodore Roosevelt and Hiram Johnson became the party's candidates in the 1912 presidential election. The proposed program included women's suffrage, direct election of senators, anti-trust legislation and the prohibition of child labour. In winning 4,126,020 votes Roosevelt defeated William H. Taft, the official candidate of the Republican Party. However, he received less votes than the Democratic Party candidate, Woodrow Wilson. In 1914, Robins was candidate for United States Senator from Illinois.
Raymond Robins, Yakov Peters, Capt. D. Heywood Hardy, Lev Karakhan, Vasili Likhachev
(behind the boy soldier), Alexander Gumberg, and Charles Stevenson Smith.
During the First World War he headed the expedition for the American Red Cross to Russia. After the abdication of Tsar Nicholas II in March, 1917, George Lvov was asked to head the new Provisional Government in Russia. Robins was a strong supporter of Alexander Kerensky, the Minister of War. Kerensky toured the Eastern Front where he made a series of emotional speeches where he appealed to the troops to continue fighting. Kerensky argued that: "There is no Russian front. There is only one united Allied front." Leon Trotsky met him during this period and described him as a "counter-revolutionary".
It has been claimed that after the Russian Revolution Robins met Lenin on average three times a week. The two men had a series of debates on politics. On one occasion Lenin said to Robins: "We may be overthrown in Russia, by the backwardness of Russia or by foreign force, but the idea in the Russian revolution will break and wreck every political social control in the world. Our method of social control dominates the future. Political social control will die. The Russian revolution will kill it everywhere." Robins dismissed this idea of the future: "But my government is a democratic government. Do you really say that the idea in the Russian revolution will destroy the democratic idea in the government of the United States?... Our national government and our local governments are elected by the people, and most of the elections are honest and fair, and the men elected are the true choices of the voters. You cannot call the American government a bought government."
Lenin replied: "I'll tell you, our system will destroy yours because it will consist of a social control which recognizes the basic fact of modern life. It recognizes the fact that real power today is economic and that the social control of today must therefore be economic also.... This system is stronger than yours because it admits reality. It seeks out the sources of daily human work-value and, out of those sources, directly, it creates the social control of the state. Our government will be an economic social control for an economic age. It will triumph because it speaks the spirit, and releases and uses the spirit, of the age that now is. Therefore, Colonel Robins, we look with confidence at the future. You may destroy us in Russia. You may destroy the Russian revolution in Russia. You may overthrow me. It will make no difference. A hundred years ago the monarchies of Britain, Prussia, Austria, Russia, overthrew the government of revolutionary France. They restored a monarch, who was called a legitimate monarch, to power in Paris. But they could not stop, and they did not stop, the middle-class political revolution, the revolution of middle class democracy, which had been started at Paris by the men of the French Revolution of 1789. They could not save feudalism. Every system of feudal aristocratic social control in Europe was destined to be destroyed by the political democratic social control worked out by the French Revolution. Every system of political democratic social control in the world to-day is destined now to be destroyed by the economic producers' social control worked out by the Russian revolution."
Raymond Robins in 1917
While in Petrograd Robins met the British journalist, Arthur Ransome, who he met for the first time in 1918. According to Roland Chambers, the author of The Last Englishman: The Double Life of Arthur Ransome (2009): "On the day after the Trotsky interview, Ransome had dined with Colonel Raymond Robins, head of the American Red Cross, and Edgar Sisson, former editor of the Chicago Tribune. Robins, he concluded, was by far the superior specimen. As an evangelical Christian, an Alaskan gold prospector and straight-talking Chicago progressive, his politics were no more compatible with Bolshevism than those of Woodrow Wilson, whom he counted amongst his personal friends. But Robins had seen the Bolsheviks at work in the regional soviets. No other government, he believed, was capable of maintaining order at home while opposing German interests. If the Allies wanted an Eastern Front, they should supply the money and arms. If they wanted peace, it would have to be a general peace. By the time Ransome returned to Petrograd, Robins was on excellent terms with Trotsky and Lenin, whom he considered men of courage and integrity." Ransome later recalled that Robins told him that Leon Trotsky was "four times son of a bitch, but the greatest Jew since Jesus."
The passengers include Alexander Gumberg, Karl Radek (with pipe), Mrs. Radek, and Trotsky's sister.
On 25th April, 1918, Robins had dinner with Arthur Ransome. The two men agreed that the Soviet government would survive and that Western governments should not attempt to overthrow it. Ransome argued that he had written a pamphlet with Karl Radek, entitled On Behalf of Russia: An Open Letter to America. Robins, who was just about to leave for home, agreed to use his influence to get it published by The New Republic magazine.
The pamphlet was published in July 1918. It included the following passage: "These men who have made the Soviet government in Russia, if they must fail, will fail with clean shields and clean hearts, having, striven for an ideal which will live beyond them. Even if they fail, they will none the less have written a page in history more daring, than any other which I can remember in the history of humanity. They are writing it amid the slinging of mud from all the meaner spirits in their country, in yours and in my own. But when the thing is over, and their enemies have triumphed, the mud will vanish like black magic at noon, and that page will he as white as the snows of Russia, and the writing on it as bright as the gold domes I used to see glittering in the sun when I looked from my window in Petrograd."
William Hard, the author of Raymond Robins' Own Story (1920), has argued that as a result of Robins arguing against Allied intervention in Russia in support of the White Army, he was accused of being a Bolshevik. "Today, because he opposes American and Allied military intervention in Russia, certain hasty or malevolent persons try to stamp the stigma of Bolshevism on him. I only ask: how many of those persons have ever said one word against Bolshevism where to say it was dangerous? Robins spoke against Bolshevism in Petrograd itself. He labored against Bolshevism., and is publicly recorded to have labored against it, all through the period while Russia was making its choice between Kerensky and Lenin. Robins has been consistently and continuously anti-Bolshevik, in America and in Russia; but he saw the failure of our diplomacy in Russia; and he had a chance to perceive the reason, the instructive reason.... Robins holds the view that no effort to combat Bolshevism can ever be successful unless it is directed against what Bolshevism is, instead of against what Bolshevism is not; and therefore he has first tried to describe what Bolshevism is, in its internal going power; and if people call him a Socialist for doing so, he can afford to be patient."
Hard went on to point out: "Robins came back from Russia in June of 1918, and said that in the Soviet republic there was great strength and great capacity for resistance. Other persons, much more in the confidence of the administration at Washington, came back and said that the Soviet republic was nothing but a street corner soap-box stuffed out with a German subsidy. For month after month, then, the Allies and the anti-Bolshevik Russians attacked the Soviet republic by arms on all fronts; and the Soviet republic, blockaded, starving, sick, with no conceivable help any longer from the Kaiser and with no possible access to the imported machine-materials on which Russian industrial life had always depended, for month after month held out. Tested by life, the Soviet republic was not a feeble thing. It was a powerful thing, generating its own power out of its own power-house, somewhere, somehow."
Robins continued to call for United States recognition and he was partly responsible for persuading President Franklin Roosevelt to exchange ambassadors in 1933.
Raymond Robins died on 26th September, 1954.
(1) William Hard, Raymond Robins' Own Story (1920)
With Bolshevism triumphant at Budapest and at Munich, and with a Council of Workmen's and Soldiers' Deputies in session at Berlin, Raymond Robins began to narrate to me his personal experiences and his observations of the dealings of the American government with Bolshevism at Petrograd and at Moscow.
But he was not merely an observer of those dealings. He was a participant in them. Month after month he acted as the unofficial representative of the American ambassador to Russia in conversations, and negotiations with the government of Lenin.
Throughout that period he saw Lenin personally three times (on an average) a week. Incidentally, the degree of eagerness felt by the Allied and American governments to make Lenin's acquaintance and to learn his actual character and his actual purpose, good or bad, may be judged from the fact that during all those months Col. Raymond Robins of the American Red Cross was the only Allied or American officer who ever actually had personal conferences with Lenin.
Lenin speaks English fluently. He was talking one day about Russia's industrial backwardness and he made a saying which Robins now calls especially to mind.
Russia's backwardness in industry is a grave handicap to Russian Socialism. Russia is poorly prepared for the socialist experiment. Lenin knew this. Whatever else he may be, he is a man of knowledge, of great knowledge, a laborious student and scholar. He was speaking of the prospects of Socialism in Russia, and he said:
"The flame of the Socialist revolution may die down here. But we will keep it at its height till it spreads to countries more developed. The most developed country is Germany. When you see a Council of Workmen's and Soldiers' Deputies at Berlin, you will know that the proletarian world revolution is born."
We see that Council to-day, and we see Allied and American diplomacy considering it and striving, in one way and another, to deal with it. That is why Colonel Robins is especially moved to speak at this moment. He saw the diplomatic methods which failed to deal successfully with Bolshevism at Petrograd and Moscow, and he feels that he has every reason of practical experience to believe that they will equally fail at Berlin and at Budapest and at Munich and at every other place where they may be tried.
The failure at Petrograd and at Moscow was complete. The United States went away from Petrograd and from Moscow diplomatically vanquished. Colonel Robins ventures to state the fundamental reason for our discomfiture. But, before stating that reason, I must, in one respect, state him.
He is the most anti-Bolshevik person I have ever known, in way of thought; and I have known him for seventeen years. When he says now that in his judgment the economic system of Bolshevism is morally unsound and industrially unworkable, he says only what I have heard him say in every year of our acquaintance since 1902.
He was living then at a settlement in Chicago called the Chicago Commons. It had an open forum called, I think I remember, the Free Floor. On that Free Floor, and in little halls on North Clark Street, and in all sorts of other places, Robins and I heard all sorts of Bolshevik oratory in the Chicago of the early innocent twentieth century. We did not need to wait for the developments of the year 1919 in order to know that Bolshevism, after all, was not invented during the Great War by the German General Staff as a war measure. We heard Bolshevism in Chicago, all of it - the Dictatorship of the Proletariate, the Producers' Republic, the Election of Legislators by Industries, the Abolition of All Classes Except the Working-class - absolutely all of it, in the oratory of sincere and eloquent fanatics almost two decades ago. And it existed long before we heard it.
But Raymond Robins' favorite intellectual pursuit was to go after it with all the arguments he could think of. In forums and in halls and at that earnest gathering of local truth-seekers called the Friday Lunch Club he never missed an opportunity to argue against Bolshevism and against every other form of Socialism. I remember that once, when he heard from some false source that I was about to make the hideous mistake of joining the Socialist party, he kept me up till two o'clock in the morning, making me one of his best public orations, all to myself, to dissuade me. He would go to any distance out of his way in order to save any brand from the Socialist burning.
To-day, because he opposes American and Allied military intervention in Russia, certain hasty or malevolent persons try to stamp the stigma of Bolshevism on him. I only ask: how many of those persons have ever said one word against Bolshevism where to say it was dangerous? Robins spoke against Bolshevism in Petrograd itself. He labored against Bolshevism., and is publicly recorded to have labored against it, all through the period while Russia was making its choice between Kerensky and Lenin. Robins has been consistently and continuously anti-Bolshevik, in America and in Russia; but he saw the failure of our diplomacy in Russia; and he had a chance to perceive the reason, the instructive reason.
(2) Roland Chambers, The Last Englishman: The Double Life of Arthur Ransome (2009)
In the space of twenty-four hours, he had met with "eighteen or nineteen folk, ranging from the current dictator of Russia preferring to Trotsky, to our ambassador through pretty much every shade of contradictory Russian opinion". Amongst the foreign missions gathered in Petrograd, the French were taking the "maddest because the angriest view", while the Americans were the most sympathetic. On the day after the Trotsky interview, Ransome had dined with Colonel Raymond Robins, head of the American Red Cross, and Edgar Sisson, former editor of the Chicago Tribune. Robins, he concluded, was by far the superior specimen. As an evangelical Christian, an Alaskan gold prospector and straight-talking Chicago progressive, his politics were no more compatible with Bolshevism than those of Woodrow Wilson, whom he counted amongst his personal friends. But Robins had seen the Bolsheviks at work in the regional soviets. No other government, he believed, was capable of maintaining order at home while opposing German interests. If the Allies wanted an Eastern Front, they should supply the money and arms. If they wanted peace, it would have to be a general peace. By the time Ransome returned to Petrograd, Robins was on excellent terms with Trotsky and Lenin, whom he considered men of courage and integrity.
When Robins came to Trotzky's door, there were soldiers there; and when he got inside, there was a man standing by Trotzky's desk who at once showed much excitement. "Kerensky-ite," he cried, pointing to Robins. "Counter-revolutionary." He had heard Robins addressing the Russian soldiers against peace and in favor of fighting Germany. "Counter-revolutionary," he continued.
Robins raised his arm in a gesture he hoped was commanding and calm, and said to his interpreter:
"Tell Commissioner Trotzky it is true I did everything I could to help Kerensky and to keep the Commissioner from getting into power."
Trotzky frowned.
"But tell the Commissioner," said Robins, "that I differ from some of my friends. I know a corpse when I see one, and I think the thing to do with a corpse is to bury it, not to sit up with it. I admit that the Commissioner is in power now."
Trotzky looked mollified.
"But tell the Commissioner," said Robins, "that if Kornilov or Kaledine or the Czar were sitting in his place, I would be talking to them."
Trotzky looked less mollified. Robins hastened to state his whole errand.
"Tell the Commissioner," he said, "that I have come to ask him: Can the American Red Cross Mission stay in Russia with benefit to the Russian people and without disadvantage to the Allied cause? If so, it will stay. If not, it will go."
Trotzky looked at Robins steadily, and considered. "What proof do you want?" he said.
Robins was prepared to ask a certain very definite proof. He mentioned it.
"I have thirty-two cars of Red Cross supplies," he said, "and I want to send them from here to Jassy in Rumania, consigned to the American Red Cross there. I want to change over from Kerensky guards to your guards, and I want those cars to go through to the Rumanian border under your Soviet frank. I want you to order your military people and your railway people to pass my train and to expedite it."
In making this request Robins had two purposes. He wanted to discover two things. First: Did the Soviet have the power to give protection to a train of supplies on its way across all central western Russia from Petrograd thirteen hundred miles to the River Pruth? Second: Would the Soviet be willing to move supplies away from the Petrograd district, where the Germans might get them, to Jassy, where the Germans were very unlikely to get them?...
One day, at Smolny, he turned to Robins and bluntly said:
"Colonel Robins, about this embargo on goods going from Russia into Germany, how would you like to put your officers on our frontier to enforce it?"
It was some time before Robins could gather his mind together to speak a word in reply. Then he said:
"Mr. Commissioner, I am not a diplomat or a general, and I can afford to be as ignorant as I am. I don't understand you. Your proposition sounds good, but it sounds too good. In America we would say there must be something on it. I have to ask you frankly, Why do you make it?"
Trotzky was annoyed. Besides his power of being passionate, he has a great power of being supercilious. He showed it now. His black eyes blazed out his impatience. One of his vices is intellectual pride. One of his virtues is that he confesses it. In public speaking he does not flatter an audience. He will even go to the other extreme. He will openly sneer at it. He will freeze it with contempt. A Russian journalist once described him, on such an occasion, as a freezing fire. His face then is the face of a Mephistopheles, diabolically intelligent, diabolically scornful, redeemed only by the eyes of much human suffering in a long and relentless pursuit of a human co-operative Utopia.
Walking through the Most Holy Gate of the Kremlin, Robins reflected again on the consequences of a class Church---its consequences in the surrender of many coming rulers of working-men's parties and of working-men's governments to influences altogether outside the Church; and he arrived in the inside of the Kremlin; and he entered the building that had been the High Court Building of the Czar; and he went up three flights of stairs to a little room with velvet hangings and with a great desk of beautiful wood beautifully worked, where the Czar had been accustomed to sit and sign certain sorts of papers.
There now sits Lenin, short-built and stanch-built, gray-eyed and bald-headed and tranquil. He wears a woolen shirt and a suit of clothes bought, one would think, many years ago and last pressed shortly afterward. His room is quite still. As he has denounced "the intoxication of the revolutionary phrase," so he seems to reject the intoxication of revolutionary excitement.
He busies himself with reports and accounts of departments on his desk and receives visitors for stated lengths of time - ten minutes, five minutes, one minute. He is likely to receive them standing. He speaks to them with the low voice of the man who does not need to raise his voice.
In his ways of easy authority one may perhaps see his father, State Councilor of the Government of Simbirsk, hereditary nobleman. In his ways of thought one certainly sees his brother, executed as a political offender by the Czar's police when Lenin was seventeen.
Robins could never visit Lenin in the Czar's High Court Building without thinking of that execution and of the sanction given to it - and to all such executions - by the Russian State Church. Back of the gallows, generation after generation, in every part of Russia, stood the priests, with their vessels of gold and their vestments of lovely weavings, and with their icons, preaching obedience to autocracy and giving the word of God to back the word of the Czar, and blessing the hangman.
Out of that background came Lenin's talk. He talked with no other assumption than that religion had departed out of the public life and out of the public policy of Russia, along with the Czar. He talked of secular effort only, of only material organization.
He said to Robins: "We may be overthrown in Russia, by the backwardness of Russia or by foreign force, but the idea in the Russian revolution will break and wreck every political social control in the world. Our method of social control dominates the future. Political social control will die. The Russian revolution will kill it everywhere."
"But," said Robins, "my government is a democratic government. Do you really say that the idea in the Russian revolution will destroy the democratic idea in the government of the United States?"
"The American government," said Lenin, "is corrupt."
"That is simply not so," said Robins. "Our national government and our local governments are elected by the people, and most of the elections are honest and fair, and the men elected are the true choices of the voters. You cannot call the American government a bought government."
"Oh, Colonel Robins," said Lenin, "you do not understand. It is my fault. I ought not to have said corrupt. I do not mean that your government is corrupt in money. I mean that it is corrupt and decayed in thought. It is living in the political thinking of a bygone political age. It is living in the age of Thomas Jefferson. It is not living in the present economic age. Therefore it is lacking in intellectual integrity. How shall I make it clear to you? Well, consider this:
"Consider your states of New York and Pennsylvania. New York is the center of your banking system. Pennsylvania is the center of your steel industry. Those are two of your most important things---banking and steel. They are the base of your life. They make you what you are. Now if you really believe in your banking system, and respect it, why don't you send Mr. Morgan to your United States Senate? And if you really believe in your steel industry, in its present organization, why don't you send Mr. Schwab to the Senate? Why do you send men who know less about banking and less about steel and who protect the bankers and the steel manufacturers and pretend to be independent of them? It is inefficient. It is insincere. It refuses to admit the fact that the real control is no longer political. That is why I say that your system is lacking in integrity. That is why our system is superior. That is why it will destroy yours."
"Frankly, Mr. Commissioner," said Robins, "I don't believe it will."
"It will," said Lenin. "Do you know what ours is?"
"Not very well yet," said Robins. "You've just started."
"I'll tell you," said Lenin. "Our system will destroy yours because it will consist of a social control which recognizes the basic fact of modern life. It recognizes the fact that real power to-day is economic and that the social control of to-day must therefore be economic also. So what do we do? Who will be our representatives in our national legislature, in our national Soviet, from the district of Baku, for instance?
"This system is stronger than yours because it admits reality. It seeks out the sources of daily human work-value and, out of those sources, directly, it creates the social control of the state. Our government will be an economic social control for an economic age. It will triumph because it speaks the spirit, and releases and uses the spirit, of the age that now is.
"Therefore, Colonel Robins, we look with confidence at the future. You may destroy us in Russia. You may destroy the Russian revolution in Russia. You may overthrow me. It will make no difference. A hundred years ago the monarchies of Britain, Prussia, Austria, Russia, overthrew the government of revolutionary France. They restored a monarch, who was called a legitimate monarch, to power in Paris. But they could not stop, and they did not stop, the middle-class political revolution, the revolution of middle class democracy, which had been started at Paris by the men of the French Revolution of 1789. They could not save feudalism.
"Every system of feudal aristocratic social control in Europe was destined to be destroyed by the political democratic social control worked out by the French Revolution. Every system of political democratic social control in the world to-day is destined now to be destroyed by the economic producers' social control worked out by the Russian revolution.
"Colonel Robins, you do not believe it. I have to wait for events to convince you. You may see foreign bayonets parading across Russia. You may see the Soviets, and all the leaders of the Soviets, killed. You may see Russia dark again as it was dark before. But the lightning out of that darkness has destroyed political democracy everywhere. It has destroyed it not by physically striking it, but simply by one flash of revealment of the future."
This utter assumption of the inevitability of things was naturally appalling to Robins. It was a denial of the essence of Americanism. The essence of Americanism is an utter assumption of human free will---of human free will determining human fate by its own visions of its own moral preferences. It follows in complete Americanism that we have to consult the conscience of every citizen, irrespective of economic or other status, and that we have to proceed in the light of the accumulated consciences of the nation, as revealed roughly in majorities.
Lenin's philosophy could not convince any American like Robins. Robins came back from Russia more anti-Socialist than when he went. But he also came back knowing that Lenin's philosophy is indeed a philosophy and that it cannot be countered by pretending that it is nothing but blood and wind. It challenges Americanism with a genuine challenge. It does not merely reject the basis of Americanism. It brings forward a strongly competitive basis of its own.
Lenin, of course, frankly, was not talking about consciences or about majorities. But neither was he talking about nothing. He was talking about vitalities, economic vitalities. He was saying:
The working-class is to-day the vital economic class in Russia. Through that class we will make a Russian government better than the Czar's or Kerensky's, because it will be more vital, and better than any political government anywhere, because it will be economic. And this system, by example, will penetrate and saturate the world.
Such was Lenin in talk.
Robins came back from Russia in June of 1918, and said that in the Soviet republic there was great strength and great capacity for resistance. Other persons, much more in the confidence of the administration at Washington, came back and said that the Soviet republic was nothing but a street corner soap-box stuffed out with a German subsidy. For month after month, then, the Allies and the anti-Bolshevik Russians attacked the Soviet republic by arms on all fronts; and the Soviet republic, blockaded, starving, sick, with no conceivable help any longer from the Kaiser and with no possible access to the imported machine-materials on which Russian industrial life had always depended, for month after month held out. Tested by life, the Soviet republic was not a feeble thing. It was a powerful thing, generating its own power out of its own power-house, somewhere, somehow.
Robins' antagonists claimed that this native internal power-house did not exist. Robins claimed that it did exist. It has been proved to exist.
So far, Robins has tried to describe certain leading characteristics of Bolshevik power and intention. He has shown the actual authority of the Soviets even in the days before the Bolsheviks controlled them; he has shown the ruinous effect of the diplomacy of Trotzky on the morale of the German Eastern army and on the morale of the proletarian element in the German population; he has shown the willingness of Trotzky and of Lenin to seek economic co-operation and to seek military co-operation with America and America's rejection of their offers; he has shown the carefully-thought-out theory of Lenin for an economic state; he has shown Lenin's willingness to concede, to compromise, in order to make some sort of state in Russia stand - stand with organization and with orderliness; and he has shown the personal and public devotion and the utter confidence which Lenin's followers give to Lenin and to Lenin's program.
Robins holds the view that no effort to combat Bolshevism can ever be successful unless it is directed against what Bolshevism is, instead of against what Bolshevism is not; and therefore he has first tried to describe what Bolshevism is, in its internal going power; and if people call him a Socialist for doing so, he can afford to be patient. Paraphrasing Lenin and rather reversing him, Robins can say to such people: "I was fighting Socialism before some of you ever thought of it, and I shall be fighting Socialism when some of you have quit."
Reform and Revolution
Making the World Safe

redpajama_set_name: RedPajamaCommonCrawl


text:
\section*{Introduction}

\paragraph{Background.}

Neuroimaging plays a prominent role in our attempt to understand the human brain, as it enables an array of analyses such as volumetry, morphology, connectivity, physiology, and molecular studies. A prerequisite for almost all these analyses is the contouring of brain structures, a task known as image segmentation. In this context, magnetic resonance imaging (MRI) is the imaging technique of choice, since it enables the acquisition of noninvasive scans \textit{in vivo} with excellent soft-tissue contrast.

The vast majority of neuroimaging studies rely on prospective datasets of high-quality MRI scans, and especially on \SI{1}{\milli\meter} T1-weighted acquisitions. Indeed, these scans present a remarkable white-grey matter contrast, and can be easily analysed with widespread neuroimaging packages (SPM~\cite{ashburner_unified_2005} FSL~\cite{jenkinson_2012_fsl} FreeSurfer~\cite{fischl_freesurfer_2012}) to derive quantitative morphometric measurements. Meanwhile, brain MRI scans acquired in the clinic (e.g., for diagnostic purposes) present much higher variability in acquisition protocols, and thus cannot be analysed with conventional neuroimaging softwares. This variability is threefold. First, clinical scans use a wide range of MR sequences and contrasts, which are chosen depending on the tissue properties to highlight. Then, they often present real-life artefacts that are uncommon in research datasets, such as very low signal-to-noise ratio, or incomplete field of view. Finally, instead of using 3D scans at high resolution like in research, physicians usually prefer to acquire a sparse set of 2D images in parallel planes, which are faster to inspect but introduce considerable variability in terms of slice spacing, thickness, and orientation.

The ability to analyse clinical datasets is highly desirable, since they represent the overwhelming majority of brain MRI scans. For example, 10 million brain clinical scans were acquired in the US in 2019 alone~\cite{oren_curbing_2019}. This figure is orders of magnitude larger than the size of the biggest research studies such as ENIGMA~\cite{hibar_common_2015} or UK BioBank~\cite{alfaro-almagro_image_2018}, which comprise tens of thousands subjects. Hence, analysing such clinical data would considerably increase the sample size and statistical power of the current neuroimaging studies. Furthermore, it would also enable the analysis of populations that are currently underrepresented in research studies (e.g., ADNI~\cite{jack_alzheimers_2008} and UK BioBank~\cite{alfaro-almagro_image_2018} with 95\% white subjects~\cite{adni_sdni_2012,fry_comparison_2017}), but that are more easily found in clinical datasets. Therefore, there is a clear need for an automated tool that can robustly segment clinical data of any contrast and resolution.

\paragraph{Related Works.}

The gold standard in brain MRI segmentation is manual delineation. However, this tedious procedure requires costly expertise and is untenable for large-scale clinical applications. Alternatively, one could only consider high-quality scans (i.e., \SI{1}{\milli\meter} T1-weighted scans) that can be analysed with neuroimaging softwares, but this would drastically decrease effective sample sizes, because such scans are expensive and seldom acquired in the clinic.

Several methods have been proposed for robust segmentation of MRI scans with variable contrast or resolution. First, contrast-adaptiveness has classically been addressed with Bayesian strategies using unsupervised likelihood model~\cite{puonti_fast_2016}. Nevertheless, the accuracy of these methods progressively deteriorates at decreasing resolutions due to the partial volume effect, where voxel intensities become less representative of the underlying tissues~\cite{choi_partial_1991}. While the partial volume effect can theoretically be modelled within the Bayesian framework~\cite{van_leemput_unifying_2003}, the resulting algorithm quickly becomes intractable at decreasing resolutions, thus precluding analysis of clinical scans with large slice thickness.

The modern segmentation literature mostly relies on supervised convolutional neural networks (CNNs)~\cite{milletari_v-net_2016,ronneberger_u-net_2015}, which obtain fast and accurate results on their training domain (i.e., scans with similar contrast and resolution). However, CNNs suffer from the ``domain-gap'' problem~\cite{pan_survey_2010}, where networks do not generalise well to data with different resolution~\cite{ghafoorian_transfer_2017} or MR contrast~\cite{akkus_deep_2017}, even within the same modality (e.g., T1-weighted scans acquired with different parameters or hardware)~\cite{karani_test-time_2021}. Data augmentation techniques have addressed this problem in intra-modality scenarios by applying spatial and intensity transforms to the training data~\cite{zhang_generalizing_2020}. However, the resulting CNNs still need to be retrained for each new MR contrast or resolution, which necessitates costly labelled images. Another approach to bridging the domain gap is domain adaptation, where CNNs are explicitly trained to generalise from a ``source'' domain with labelled data, to a specific ``target'' domain, where no labelled examples are available~\cite{chen_synergistic_2019,karani_test-time_2021}. Although these methods alleviate the need for supervision in the target domain, they still need to be retrained for each new domain, which makes them impractical to apply at scale on highly heterogeneous clinical data.

Very recently, we proposed \textit{SynthSeg}~\cite{billot_synthseg_2021}, the first method that can segment brain scans of any contrast and resolution without retraining. This was achieved by adopting a domain randomisation approach~\cite{tobin_domain_2017}, where a 3D CNN is trained on synthetic scans of fully randomised contrast and resolution. Consequently, \textit{SynthSeg} learns domain-agnostic representations, which provide it with an outstanding generalisation ability compared with previous methods~\cite{billot_synthseg_2021}. However, \textit{SynthSeg} frequently falters when applied to clinical scans with low signal-to-noise ratio, poor tissue contrast, or acquired at very low resolution -- an issue that we address in the present article.

Several strategies have been introduced to improve the robustness of CNNs, most notably hierarchical models. These models divide the final task into easier operations such as: progressive refining of segmentations at increasing resolutions~\cite{isensee_nnu-net_2021}, or segmenting the same image with increasingly finer labels~\cite{roth_application_2018}. Although hierarchical models can help improving performance, they may still struggle to produce topologically plausible segmentations in difficult cases, which is a well-known problem for CNNs~\cite{nosrati_incorporating_2016}. Recent approaches have sought to solve this problem by modelling high-order topological relations, either by aligning predictions and ground truths in latent space during training~\cite{oktay_anatomically_2018}, by correcting predictions with a registered atlas~\cite{sinclair_2022_atlas}, or with denoising networks~\cite{larrazabal_post-dae_2020}.

While the aforementioned methods substantially improve robustness, they do not guarantee accurate results in every case. Hence, the ability to identify erroneous predictions is crucial, especially when analysing clinical data of varying quality. Traditionally, this has been achieved with visual quality control (QC), but several automated strategies have now been proposed to replace this tedious procedure. A first class of methods seek to register predictions to a pool of reference segmentations to compute similarity scores~\cite{valindria_reverse_2017}, but the required registrations remain time-consuming. Therefore, recent techniques employ fast CNNs to model QC as a regression task, where faulty segmentations are rejected by applying a thresholding criterion on the regressed scores~\cite{kohlberger_evaluating_2019,liu_alarm_2019,wang_deep_2020}.

\paragraph{Contributions.}

In this article, we present \textit{SynthSeg$^{+}$}, the first segmentation suite for robust analysis of clinical brain MRI scans of any contrast and resolution. Specifically, the proposed method leverages a novel deep learning architecture composed of hierarchical networks and denoisers. This new architecture is trained with the domain randomisation approach introduced by \textit{SynthSeg}, and is shown to considerably increase the robustness of the original method. Furthermore, \textit{SynthSeg$^{+}$} includes new modules for cortex parcellation, automated failure detection, and estimation of intracranial volume (ICV, a crucial covariate in volumetry). All aspects of our method are thoroughly evaluated on more than 15,000 highly heterogeneous clinical scans, where \textit{SynthSeg$^{+}$} is shown to enable, for the first time, automated segmentation and volumetry of large, uncurated clinical datasets. A preliminary version of this work was presented at MICCAI 2022~\cite{billot_robust_2021}. Here we extend this work by adding cortical parcellation, automated QC, and ICV modules, as well as by evaluating our approach in five new experiments. The proposed method can be run with FreeSurfer by executing the simple following command:
\begin{verbatim}
mri_synthseg --i [input scan] --o [output segmentation] --robust.
\end{verbatim}

\section*{Results}

\begin{figure}[t]
\includegraphics[width=\textwidth]{figures/overview.pdf}
\caption{Overview of \textit{SynthSeg$^{+}$}. (a) Inference pipeline. (b) Outputs of the intermediate modules for three representative cases, each displayed on a different row. First, all modules produce accurate results. Second, the denoiser corrects mistakes in the initial tissue labels (red boxes), ultimately leading to a good segmentation. Third, all modules obtain poor results due to the very low tissue contrast of the input, but the automated QC correctly identifies the output as unusable for subsequent analyses.}
\label{fig:overview}
\end{figure}

\paragraph{A multi-task segmentation suite.}
\textit{SynthSeg$^{+}$} uses hierarchical modules designed to efficiently decompose the segmentation task into easier intermediate steps that are less prone to errors (Figure~\ref{fig:overview}). Specifically, a first network $S_1$ produces preliminary segmentations of four coarse tissue classes, which are corrected for potential topological mistakes by a denoiser $D$ (see red boxes in Figure~\ref{fig:overview}.b). Next, predictions of the target labels are obtained with a segmenter $S_2$. The obtained cortical region is then further parcellated using a segmenter $S_3$. Finally, a regressor $R$ provides us with ``QC scores'' (describing the quality of the obtained segmentations) for 10 regions, which we use to perform automated QC. We emphasise that all segmentations are given at \SI{1}{\milli\meter} isotropic resolution, regardless of the native resolution of the input.

In the following experiments, we first quantitatively evaluate the accuracy of \textit{SynthSeg$^{+}$} for whole-brain segmentation and cortex parcellation. Next, we assess the performance of the automated QC module for automatic failure detection. We then present results obtained for ICV estimation, and a proof-of-concept volumetric study for detection of Alzheimer's Disease patients. Finally, we demonstrate \textit{SynthSeg$^{+}$} in a real-life study of ageing conducted on more than 14,000 uncurated and heterogeneous clinical scans.

\begin{figure}[t]
\includegraphics[width=\textwidth]{figures/segmentations.pdf}
\caption{Segmentations obtained by all tested methods. (a) Comparison between whole-brain segmentations produced by \textit{SynthSeg$^+$} and \textit{SynthSeg}. Here we show the results obtained for three cases, where \textit{SynthSeg} respectively exhibits large (``big fail''),  moderate (``mild fail''), and no errors (``pass''). Yellow arrows point at notable mistakes. \textit{SynthSeg$^{+}$} produces excellent results given the low SNR, poor tissue contrast, or low resolution of the input scans. (b) Segmentations obtained on the same scans by three variants of our method. Note that appending $D$ substantially smooths segmentations.}
\label{fig:segmentations}
\end{figure}

\begin{figure}[t]
\includegraphics[width=\textwidth]{figures/dice_segm.pdf}
\caption{Dice scores for whole-brain segmentations. (a) Results obtained on 500 heterogeneous clinical scans, presented based on a visual QC of \textit{SynthSeg} segmentations. (b) Results obtained on scans of four MRI modalities at decreasing resolutions: T1-weighted (T1), T2-weighted (T2), DBS, and proton density (PD).}
\label{fig:dice_segm}
\end{figure}

\paragraph{Whole-brain segmentation on clinical data.}
In this first experiment, we quantitatively assess the accuracy of \textit{SynthSeg$^{+}$} for whole-brain segmentation of clinical acquisitions. In this purpose, we use 500 heterogeneous labelled scans, that we take from the picture archiving communication system (PACS) of Massachusetts General Hospital. Here, we compare \textit{SynthSeg$^{+}$} to \textit{SynthSeg}~\cite{billot_synthseg_2021} and three ablated versions obtained as follows. First, we evaluate an architecture representative of classical cascaded networks ($S_1 + S_2$), simply by ablating the denoiser $D$. Then, we evaluate two more variants obtained by appending a denoiser to \textit{SynthSeg} (\textit{SynthSeg$+ D$}) and the cascaded networks ($S_1 + S_2 + D$), with the aim of assessing the performance of state-of-the-art denoising methods~\cite{larrazabal_post-dae_2020}.

For visualisation purposes, we split the results into three classes based on a visual QC performed on the segmentations of \textit{SynthSeg}: ``big fails'', ``mild fails'' and ``passes'' (Figure~\ref{fig:segmentations}). The results, shown in Figure~\ref{fig:dice_segm}a, reveal that the hierarchical design of \textit{SynthSeg$^{+}$} considerably improves robustness, and yields the best scores in all three categories. \textit{SynthSeg$^{+}$} shows an outstanding improvement of 23.5 Dice points over \textit{SynthSeg} for big fails, and outperforms it by 5.1 and 2.4 points on mild fails and passes, respectively.

In comparison with cascaded networks, using a denoiser $D$ in \textit{SynthSeg$^{+}$} to correct the mistakes of $S_1$ leads to a consistent improvement by 2.3 to 5.1 Dice points. Additionally, integrating $D$ within our framework (rather than using it for postprocessing) enables us to substantially outperform the two other variants by 3.1 to 6.2 Dice points. This outcome is explained by the fact that denoisers return very smooth segmentations, especially for the convoluted cortex (Figure~\ref{fig:segmentations}). While the denoiser $D$ of our method also exhibits important smoothing effects (Figure~\ref{fig:overview}), we emphasise that these are successfully recovered by $S_2$, which produces segmentations with sharp boundaries.

\paragraph{Segmentation accuracy as a function of resolution.}
We now evaluate the performance of \textit{SynthSeg$^{+}$} as a function of resolution. Here, we use high-resolution research scans (i.e., at \SI{1}{\milli\meter} isotropic resolution) of different MR contrasts, and artificially downsample them at progressively decreasing resolutions. Specifically, we downsample 20 T1-weighted, 18 T2-weighted, 8 proton density (PD), and 18 deep brain stimulation (DBS) scans to nine different resolutions: 3, 5 and \SI{7}{\milli\meter} in axial, coronal, and sagittal orientation.

The results are displayed in Figure~\ref{fig:dice_segm}b and show that both \textit{SynthSeg} and \textit{SynthSeg$^{+}$} maintain an outstanding level of accuracy across all tested contrasts and resolutions (above 80 Dice points). The two methods yield very similar scores, but with different trends: while \textit{SynthSeg} produces slightly sharper and more accurate segmentations at \SI{1}{\milli\meter} isotropic resolution (up to 1.4 Dice points better), \textit{SynthSeg$^{+}$} remains more robust at lower resolutions, where it obtains superior scores for all MRI modalities (maximum gap of 1.6 Dice points).

\begin{figure}[t]
\includegraphics[width=\textwidth]{figures/dice_parc.pdf}
\caption{Dice scores obtained by the cortex parcellation module for the same data as in Figure~\ref{fig:dice_segm}.}
\label{fig:dice_parc}
\end{figure}

\paragraph{Cortex parcellation.}
Here, we assess the accuracy of \textit{SynthSeg$^{+}$} for cortex parcellation. Figure~\ref{fig:dice_parc}a shows that, as in the first experiment, \textit{SynthSeg$^{+}$} vastly improves the results of \textit{SynthSeg} on heterogeneous clinical data: it yields better scores by 17.1 Dice points on big fails, and remains superior by 1.8 points on good cases. Regarding the evolution of performance at decreasing resolutions, Figure~\ref{fig:dice_parc}b confirms the trend observed for whole-brain segmentation: although \textit{SynthSeg} is more accurate at high resolution (largest gap of 1.6 Dice points for \SI{1}{\milli\meter} T1-weighted scans), it is outperformed by \textit{SynthSeg$^{+}$} at lower resolutions (2.6 points for \SI{7}{\milli\meter} proton density scans). We note that the scores for cortex parcellation are below those obtained for whole-brain segmentation, which is not surprising since cortical regions are smaller and more convoluted, and thus more difficult to segment. Nevertheless, the quality of the present results is remarkable, since they are similar to the scores obtained by a state-of-the-art supervised CNN trained on T1-weighted scans~\cite{zhang_generalizing_2020}.

\begin{table}[t]
\centering
\setlength\tabcolsep{3pt}
    \caption{Results of the automated QC analysis on 594 clinical heterogeneous scans for \textit{SynthSeg$^{+}$} and two competitors. The best score for each metric is in bold.}
    \begin{tabular}{l c c c c c}
        \toprule
         Method & Sensitivity & Specificity & Accuracy & AUC \\
        \midrule
        $D$ vs. $S_2$ outputs & 0.934 & 0.667 & 0.882 & 0.857 \\
        Liu \textit{et al.}~\cite{liu_alarm_2019} & 0.981 & 0.905 & 0.967 & 0.990 \\
        Regressor $R$ (\textit{SynthSeg$^{+}$}) & \textbf{0.998} & 0.905 & \textbf{0.980} & \textbf{0.997} \\
        \bottomrule
        \label{tab:qc}
    \end{tabular}
\end{table}

\paragraph{Automated quality control.}
In order to test the automated QC module of \textit{SynthSeg$^{+}$}, we reuse the 500 clinical scans employed in previous experiments, and add 94 new scans that were almost unusable due to insufficient field of view, wrong organs, critical artefacts, etc. These 594 scans are segmented with \textit{SynthSeg$^{+}$}, and classified with a visual QC on a pass/fail basis. This analysis results in a rejection rate of 17.3\% for \textit{SynthSeg$^{+}$}, while this rate falls to an outstanding 3.8\% when excluding the unusable scans. We now seek to automatically replicate the results of this visual QC. Here, \textit{SynthSeg$^{+}$} is set to reject a segmentation if at least one region obtains an automated QC score below 0.65. We compare \textit{SynthSeg$^{+}$} against two competitors: a state-of-the-art technique for regression of QC scores~\cite{liu_alarm_2019}, and a simpler version of this work, where segmentation quality is estimated by computing Dice scores between the outputs of $D$ and $S_2$ (the labels of the latter being converted to the four tissue classes).

Table~\ref{tab:qc} reports the sensitivity, specificity, accuracy, and area under the ROC curve (AUC)~\cite{bradley_use_1997} obtained by each method for this binary classification task. Despite its relative simplicity, the approach comparing the outputs of $D$ and $S_2$ already yields a fair accuracy: it correctly classifies 88.2\% of the cases, albeit with limited specificity (66.7\%). While the two other strategies vastly improve the results (with accuracies above 96\%), it appears that the simple regressor used in \textit{SynthSeg$^{+}$} obtains scores very similar to the dedicated state-of-the-art method~\cite{liu_alarm_2019}: although no statistical difference can be inferred between AUCs (DeLong test~\cite{delong_comparing_1988}), our approach slightly outperforms Liu et al. for all metrics other than specificity.

\begin{figure}[t]
\centering
\includegraphics[width=0.6\textwidth]{figures/icvs.pdf}
\caption{Scatter plot of ICVs predicted by \textit{SynthSeg$^{+}$} and FreeSurfer~\cite{fischl_freesurfer_2012} on the 500 clinical scans. Orange points depict \SI{1}{\milli\meter} T1-weighted acquisitions ($N=62$), while the others scans are represented in green. The grey dashed line marks where abscissa is equal to ordinate. The Pearson correlation coefficient between the two sets of points is 0.910 when considering all scans, and 0.906 for T1-weighted scans only.}
\label{fig:icvs}
\end{figure}

\paragraph{Intracranial volume estimation.}
ICV estimation is a crucial task in volumetric studies, where the volumes of individual structures need to be corrected for head-size effects. Here, we use the clinical dataset of 500 scans to compute correlation coefficients between the ICVs estimated with \textit{SynthSeg$^{+}$} and FreeSurfer~\cite{fischl_freesurfer_2012}. First, we focus on a subset of 62 T1-weighted scans at \SI{1}{\milli\meter} resolution, since analysing these high-quality scans will provide us with a theoretical upper bound for correlation. For these scans, Figure~\ref{fig:icvs} illustrates that both methods produce strongly correlated ICVs (Pearson's $r=0.910$). Remarkably, the result hardly changes when extending this analysis to all the 500 scans ($r=0.906$), which highlights the robustness of our ICV estimation module against clinical data of varying quality. Finally, a closer inspection of Figure~\ref{fig:icvs} reveals that \textit{SynthSeg$^{+}$} tends to predict lower values than FreeSurfer for bigger heads. This observation is consistent with the literature, where similar results have been reported when comparing FreeSurfer to ICVs derived from manual segmentations~\cite{malone_2015_accurate}.

\begin{table}[!ht]
\centering
\setlength\tabcolsep{7pt}
    \caption{Effect sizes for hippocampal volumes predicted by FreeSurfer, \textit{SynthSeg}, and \textit{SynthSeg$^{+}$} between 50 controls and 50 Alzheimer's Disease patients. All subjects were imaged with \SI{1}{\milli\meter} T1-weighted scans, as well as \SI{5}{\milli\meter} axial FLAIR scans. AUC scores obtained by every method are shown in parentheses. All approaches produce very similar results, and no statistical difference can be inferred (DeLong tests on AUCs all result in p-values above 0.3).}
    \begin{tabular}{l c c c c c}
        \toprule
         Contrast & Resolution & FreeSurfer & \textit{SynthSeg} & \textit{SynthSeg$^{+}$}  \\
        \midrule
        T1 & \SI{1}{\milli\meter}$^3$ & 1.38 (0.895) & 1.40 (0.898) & 1.36 (0.891) \\
        FLAIR & \SI{5}{\milli\meter} axial & - & 1.23 (0.876) & 1.20 (0.872) \\
        \bottomrule
    \label{tab:cohens_d}
    \end{tabular}
\end{table}

\paragraph{Alzheimer's Disease volumetric study.}
We now conduct a proof-of-concept volumetric analysis, where we study whether \textit{SynthSeg$^{+}$} can detect subtle hippocampal atrophy linked with Alzheimer's Disease~\cite{gosche_hippocampal_2002}. For this purpose, we use a new dataset of 100 subjects, half of whom are diagnosed with Alzheimer's Disease. All subjects are imaged with \SI{1}{\milli\meter} T1-weighted scans, and fluid-attenuated inversion recovery (FLAIR) scans at \SI{5}{\milli\meter} axial resolution, which enables us to study performance across images of varying quality. The predicted hippocampal volumes are corrected for age, gender, and ICV (estimated with \textit{SynthSeg$^{+}$}) using a linear model. Differences between control and diseased populations are then measured by computing effect sizes (Cohen's d~\cite{cohen_statistical_1988}).

Table~\ref{tab:cohens_d} reports the results obtained by FreeSurfer, \textit{SynthSeg} and \textit{SynthSeg$^{+}$}. It shows that all methods yield strong effect sizes (between 1.36 and 1.40) on the T1-weighted scans. While segmenting the hippocampus is of modest complexity in \SI{1}{\milli\meter} T1-weighted scans, this task becomes much more challenging on the axial FLAIR scans, where the hippocampus only appears in very few slices (often just 2 to 4). Nonetheless, both \textit{SynthSeg} and \textit{SynthSeg$^{+}$} maintain a remarkable level of accuracy, and are still able to detect strong effect sizes (1.23 and 1.20, respectively). We emphasise that the results obtained are very similar across methods, which is confirmed by the absence of statistical difference when running DeLong tests on corresponding AUCs (all p-values are above 0.3).

\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/ageing_all_new.pdf}
\caption{Volume trajectories obtained by processing 14,752 heterogeneous clinical scans with SynthSeg$^+$. For each brain region, the value of $N$ indicates the number of volumes considered to build the plot, i.e., the number of segmentations that passed the automated QC for this structure. We emphasise that the obtained results are remarkably similar to recent studies, which exclusively employed scans of much higher quality~\cite{bethlehem_brain_2022,coupe_towards_2017,dima_subcortical_2022}.}
\label{fig:ageing_all}
\end{figure}

\begin{figure}[!ht]
\centering
\includegraphics[width=\textwidth]{figures/ageing_cortex_new.pdf}
\caption{Cortical and hippocampal volume trajectories obtained by \textit{SynthSeg} and \textit{SynthSeg$^+$}. This includes different scenarios: using all available scans, keeping only those which passed the automated QC ($N = 13,028$), and simulating the case where we only have access to scans acquired at low resolution (here with a slice thickness of more than \SI{6.5}{\milli\meter}). We highlight that \textit{SynthSeg$^+$} is much more robust than \textit{SynthSeg} since it outputs far fewer outliers, and is able to accurately detect atrophy patterns even for scans at very low resolutions.}
\label{fig:ageing_cortex}
\end{figure}

\paragraph{Ageing study on over 14,000 clinical scans.}
In this final experiment, we conduct a proof-of-concept clinical volumetric study on 14,752 scans from 1,367 patients with neurology visits at the Massachusetts General Hospital. Specifically, we verify whether \textit{SynthSeg$^{+}$} is able to reproduce well-established atrophy patterns related to ageing. We first process all scans with \textit{SynthSeg$^{+}$}, and filter the results with the automated QC module. Instead of rejecting whole segmentations based on a global threshold as before, we now apply a per-structure rejection criterion, where we only keep the volumes corresponding to regions with a QC score above 0.65. This provides us with between 12,954 (cortex) and 13,357 (white matter) volumes for each region. We then build age-volume trajectories independently for each region with a B-spline model, where volumes are linearly corrected for gender and the native resolution of the scans. Remarkably, Figure~\ref{fig:ageing_all} shows that the obtained trajectories are highly similar to the results obtained by recent studies on scans of much higher quality (i.e., \SI{1}{\milli\meter} T1-weighted scans)~\cite{bethlehem_brain_2022,coupe_towards_2017,dima_subcortical_2022}. For example, \textit{SynthSeg$^{+}$} accurately replicates: the peak in white-matter volume at approximately 30 years-old, the acute increase in ventricular volume for ageing subjects, and the early onset of thalamic atrophy compared to other subcortical structures.

If we now compare it to \textit{SynthSeg}, the proposed approach exhibits similar trajectories, but it does so by producing far fewer outliers (Figure~\ref{fig:ageing_cortex}). This can be seen by the substantially cleaner curves obtained by \textit{SynthSeg$^{+}$} when including volumes from all available scans, or by the considerably higher number of segmentations that passed the automated QC. Finally, as opposed to \textit{SynthSeg}, \textit{SynthSeg$^{+}$} produces almost identical results when we  evaluate it exclusively on scans of very low-resolution (i.e., with slice spacing above \SI{6.5}{\milli\meter}), which further highlights its ability to analyse clinical datasets of widely varying quality.

\section*{Discussion}

In this article we present \textit{SynthSeg$^{+}$}, the fist segmentation suite for large-scale analysis of highly heterogeneous clinical brain MRI scans. Specifically, the proposed method leverages a novel deep learning architecture, where state-of-the-art domain-agnostic CNNs are employed to perform the target segmentation task in a hierarchical fashion. As a result, \textit{SynthSeg$^{+}$} exhibits an unparalleled robustness, and is the first method that can accurately segment clinical data, including scans of poor tissue contrast, low signal-to-noise ratio, or very low resolution. Finally, our highly precise QC module automatically detects the few erroneous segmentations, which enables us to discard them from subsequent analyses.

The performance of our hierarchical modules is demonstrated throughout all experiments, where it obtains considerably better results than \textit{SynthSeg}, both quantitatively (higher Dice scores in the first experiment), and qualitatively (far fewer outliers in the ageing study). Further ablation studies show that using a denoiser $D$ to correct the mistakes of $S_1$ leads to a consistent improvement over cascaded networks. Moreover, \textit{SynthSeg$^{+}$} substantially outperforms state-of-the-art denoising networks for postprocessing~\cite{larrazabal_post-dae_2020}, which is explained by two reasons. First, these methods do not have access to the input scans, and may thus produce segmentations that deviate from the original anatomy. In contrast, we predict final segmentations by exploiting both the input test scans and prior information given by $D$. Second, the predictions of denoisers are often excessively smooth. Here, we greatly mitigate this issue by using the outputs of $D$ as robust priors for $S_2$, which effectively learns to refine the smooth boundaries given by the denoiser. However, Figure~\ref{fig:segmentations} illustrates that the predictions of \textit{SynthSeg$^{+}$} may still exhibit marginal smoothing effects. While the resolution studies show that this residual smoothness leads to a marginally lower accuracy than \textit{SynthSeg} for scans at high resolution (relatively uncommon in clinical settings), we consider this to be a minor limitation compared to the considerable gain in robustness brought by the proposed approach.

\textit{SynthSeg$^{+}$} is also the first method to perform volumetric cortex parcellation of clinical scans in the wild. The high accuracy of this module is demonstrated by also evaluating it on high-quality scans, for which it yields competitive scores with state-of-the-art supervised CNNs, either tested on the same scans (i.e., T1-baseline~\cite{zhang_generalizing_2020}), or on different datasets~\cite{henschel_fastsurfer_2020}. Remarkably, \textit{SynthSeg$^{+}$} maintains this high level of performance across all tested domains, including the highly heterogeneous clinical acquisitions. We emphasise that this outstanding generalisation ability can difficultly be matched by supervised CNNs, which would need to be retrained on every single domain using costly training labelled data.

The results have also shown that the proposed regression-based QC strategy accurately detects the few cases where \textit{SynthSeg$^{+}$} fails, which are mainly due to acquisitions of poor quality (e.g., very low SNR, or insufficient coverage of the brain). Interestingly, our method produces slightly better results than the tested state-of-the-art approach~\cite{liu_alarm_2019}, despite the fact that the latter was shown to outperform strategies based on direct regression in a simpler problem (3D segmentation with only one label)~\cite{liu_alarm_2019}. This outcome can be explained by the fact that variational auto-encoders (which are at the core of \cite{liu_alarm_2019}) have been shown in our experiments to produce excessive smoothness when dealing with whole-brain segmentations.
We also emphasise that other methods for automated QC were not evaluated in this work, since they rely on lengthy iterative processes~\cite{valindria_reverse_2017,wang_deep_2020}, which makes them impractical to use at the scale of the target clinical datasets.

Finally, we have demonstrated the true potential of \textit{SynthSeg$^{+}$} on more than 14,000 uncurated clinical scans, where it accurately replicates volume trajectories observed on data of much higher quality. This result suggests that \textit{SynthSeg$^{+}$} can be used to investigate other population effects on huge amounts of clinical data, which will considerably increase the statistical power of the current research studies. Moreover, \textit{SynthSeg$^{+}$} also unlocks other potential applications, such as the analysis of scans acquired with the promising portable low-field MRI scanners, or the introduction of quantitative morphometry in the clinic for diagnosis purposes and monitoring disease progression.

We emphasise that \textit{SynthSeg$^+$} can be run ``out of the box'' on brain MRI scans of any contrast and resolution, which has three main benefits. First, it greatly facilitates the use of our method by researchers around the world, since it eliminates the need for retraining, and thus the associated requirements in terms of labelled data, deep learning expertise, and hardware. Second, relying on a single model considerably improves the reproducibility of the results, since no hyperparameter tuning is required. And finally, it makes \textit{SynthSeg$^+$} easier to disseminate, which is done here by distributing it with the publicly available package FreeSurfer.

Future work will focus on surface extraction for estimation of cortical thickness, which is a powerful biomarker for the progression of many neuropsychiatric disorders. Surface placement has typically been achieved by representing the white matter and pial surfaces as topologically correct meshes, and deforming them to the 3D image while avoiding intersections~\cite{fischl_freesurfer_2012,hoopes_topofit_2022}. While surface placing may be of general limited accuracy when analysing clinical scans at low resolution (since the slice thickness can be locally larger than the size of cortical folds), we believe that meaningful measurements can still be extracted in regions where the surface is orthogonal to the acquisition direction.

Overall, by enabling robust and reproducible analysis of heterogeneous clinical brain MRI scans, we believe that the present work will enable the development of clinical neuroimaging studies with sample sizes considerably higher than those found in research, which has the potential to revolutionise our understanding of the healthy and diseased human brain.

\section*{Methods}

\paragraph{Training datasets and population robustness.}
The proposed method is trained on 1,020 \SI{1}{\milli\meter} T1-weighted scans from three datasets: 20 from the OASIS database~\cite{marcus_open_2007}, 500 from the ADNI~\cite{jack_alzheimers_2008}, and 500 from the Human Connectome Project (HCP)~\cite{van_essen_human_2012}. These scans are available with manual (OASIS) or automated segmentations (HCP and ADNI) for 31 brain structures~\cite{fischl_whole_2002}. Moreover, we complemented each map with 11 automated labels for extra-cerebral regions~\cite{puonti_accurate_2020}, and with 68 labels obtained with FreeSurfer for cortex parcellation. We emphasise that, while HCP subjects are young and healthy, ADNI contains ageing subjects, some diagnosed with Alzheimer's Disease and mild cognitive impairment, who frequently exhibit extensive atrophy and white matter lesions. Therefore, using such a diverse population enables us to model a wide range of morphology.

\paragraph{Brain MRI test datasets.}
Our experiments feature three datasets. The first one comprises 15,346 clinical scans from the PACS of Massachusetts General Hospital. These scans are from 1,367 MRI sessions of distinct subjects (745 males, 622 females, ages 18-90). Importantly, all scans are uncurated, and span a huge range of resolutions and MR contrasts (T1-weighted, T2-weighted, FLAIR, diffusion MRI, etc.). Ground truths for whole-brain segmentation and cortex parcellation were obtained for a subset of scans as follows. First, we isolated all sessions ($N=62$) with T1-weighted scans at approximately \SI{1}{\milli\meter} isotropic resolution, which we processed with FreeSurfer~\cite{fischl_freesurfer_2012}. Next, we rigidly registered~\cite{modat_fast_2010} the resulting labels to the remaining scans of the corresponding sessions, providing us with ground truth segmentations for 614 scans. Note that we also obtained ICVs for all these scans by reporting the estimations given by FreeSurfer on the T1-weighted scans. We then conducted a visual QC, and removed 94 scans either due to: insufficient coverage of the brain, wrong organ, empty acquisition, etc. Finally, we split the 520 remaining scans between validation (20), and testing (500), while the other 14,752 scans were held-out for indirect evaluation.

The second dataset consists of 66 scans from three sub-datasets: 20 T1-weighted scans from OASIS database~\cite{marcus_open_2007}; 18 subjects imaged twice with T2-weighted acquisitions and a sequence typically used in deep brain stimulation (DBS)~\cite{iglesias_probabilistic_2018}; and 8 proton density scans~\cite{fischl_sequence-independent_2004}. All scans are at \SI{1}{\milli\meter} isotropic resolution, and are available with either manual or semi-automated segmentations for 31 brain regions~\cite{fischl_whole_2002}. Labels for cortex parcellation were obtained by running FreeSurfer on the T1-weighted scans, or on companion \SI{1}{\milli\meter} T1-weighted acquisitions for the T2-weighted, DBS, and proton density scans.

The last dataset is a subset of 100 scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI)~\cite{jack_alzheimers_2008}, including 47 males and 53 females, aged 72.9 $\pm$ 7.6 years. Importantly, half of the subjects are healthy, while the others are diagnosed with Alzheimer's Disease. All subjects are imaged with two acquisitions: \SI{1}{\milli\meter} isotropic T1-weighted scans, and FLAIR scans at \SI{5}{\milli\meter} axial resolution. Volumetric measurements for individual regions and ICVs were retrieved for all subjects by processing the T1-weighted scans with FreeSurfer. The ADNI was launched in 2003 by the National Institute on Ageing, the National Institute of Biomedical Imaging and Bioengineering, the Food and Drug Administration, pharmaceutical companies and non-profit organisations, as a 5-year public-private partnership. For up-to-date information, see \url{adni-info.org}.

\paragraph{Deep hierarchical segmentation.}
The proposed architecture relies on four CNN modules, which are designed to split the target segmentation task into easier intermediate operations. Specifically, a first segmenter $S_1$ is trained to produce initial segmentations of four coarse labels (cerebral white matter, cerebral grey matter, cerebrospinal fluid, and cerebellum). These labels group brain regions of similar tissue types and intensities, and are thus easier to discriminate than individual structures. The output of $S_1$ is then fed to a denoising network $D$~\cite{larrazabal_post-dae_2020}, which seeks to correct potential topological inconsistencies and larger segmentation mistakes that sometimes occur for scans with poor tissue contrast, or low signal-to-noise ratio. Final whole-brain segmentations are obtained by a second segmenter $S_2$, which takes as inputs the image and the corrected preliminary segmentations. As such, $S_2$ learns to segment the 31 individual target regions by using the coarse tissue segmentations as priors. We note that $S_2$ is given the opportunity to refine the boundaries produced by $D$, which are sometimes excessively smooth. Finally, the segmentations of $S_2$ are completed by passing them to a fourth network $S_3$, which is trained to parcellate the left and right cortex labels from $S_2$ into 68 different substructures.

\paragraph{Training of the segmenters.}
The three segmentation CNNs are trained separately with extremely diverse synthetic data sampled from a generative model inspired by the Bayesian segmentation framework. Crucially, in order to train domain-agnostic networks, we adopt the domain randomisation strategy introduced in our previous work~\cite{billot_synthseg_2021}. Specifically, we draw all the parameters governing this generative model at each minibatch from uninformative uniform priors of very large range. Therefore, the segmenters are exposed to vastly changing examples in terms of shapes, MR contrast, resolution, artefacts (bias field, noise), and even morphology (due to the population variability that we use in our training set). As a result, the segmenters are forced to learn contrast and resolution-agnostic features, which enables them to be applied to brain MRI scans of any domain, without requiring any retraining.

Briefly, this generative model only requires a set of \SI{1}{\milli\meter} isotropic label maps as inputs, and synthesises training examples as follows. At each minibatch, we randomly select one of the training maps, and geometrically augment it with a random spatial transform~\cite{zhang_generalizing_2020}. Next, a preliminary image is built by sampling a randomised Gaussian mixture model conditionally on the deformed label map~\cite{billot_learning_2020}. The resulting image is then obtained by consecutively applying a random bias field, noise injection, intensity rescaling between 0 and 1, and a random voxel-wise exponentiation~\cite{zhang_generalizing_2020}. In turn, low-resolution and PV effects are modelled with Gaussian blurring and sub-sampling at random low resolution. Finally, training pairs are obtained by: defining the deformed label map as ground truth, and resampling the low-resolution images back to the \SI{1}{\milli\meter} isotropic grid, such that the downstream segmenters are trained to operate at high resolution~\cite{billot_partial_2020}.

\paragraph{Training of the denoiser.}
State-of-the-art methods for denoising and topological correction rely mainly on supervised CNNs that are trained to recover ground truth segmentations from artificially corrupted versions of the same maps~\cite{karani_test-time_2021,khan_deep_2021,larrazabal_post-dae_2020}. However, the strategies used to corrupt the input segmentations are often handcrafted (random erosion and dilation, swapping of labels, etc.), and thus do not accurately capture the type of errors made by the segmentation method to correct. In order to train $D$ with examples that are representative of $S_1$ errors, we instead degrade real images and feed them to the trained (and frozen) $S_1$. $D$ is then trained to map the outputs of $S_1$ back to their ground truth. During training, images are degraded on the fly with operations similar to the training of segmenters: spatial deformation, bias field, voxel-wise exponentiation, simulation of low resolution, and noise injection. However, the ranges of the parameters controlling these corruptions are made considerably wider than for the training of segmenters, in order to more frequently obtain erroneous segmentations from $S_1$, and thus to enrich the training data.

\paragraph{Automated QC module.}
While the proposed architecture considerably improves robustness, it remains important to detect potential erroneous predictions, especially when segmenting clinical scans of varying quality. Therefore, we introduce another module for automated failure detection. More precisely, we train a regressing network $R$ to predict ``performance scores'' for 10 representative regions of interest (white matter, cortex, lateral ventricle, cerebellum, thalamus, hippocampus, amygdala, pallidum, putamen, brainstem), based solely on the segmentations produced by $S_2$. Here, the performance scores aim to reflect Dice scores that would have been obtained if the input scans were available with associated ground truths~\cite{hann_quality_2019}. The segmentation of a region is then classified as failed if it obtains a predicted Dice score lower than 0.65 (a value chosen based on the validation set). In practice, $R$ is trained with the same method as $D$, where we degrade real images on the fly, segment them with $S_2$, and feed the obtained segmentations to $R$.

\paragraph{Network architectures.}
All the segmentation modules use the same architecture as \textit{SynthSeg}~\cite{billot_learning_2020}, which is based on a 3D UNet~\cite{ronneberger_u-net_2015}. Briefly, it comprises five levels, each consisting of two convolutions, a batch normalisation, and either a max-pooling (contracting path), or upsampling operation (expanding path). Every convolution employs a 3$\times$3$\times$3 kernel and an Exponential Linear Unit activation~\cite{clevert_fast_2016}, except for the last layer, which uses a softmax. The first layer counts 24 features, while this number is doubled after each max-pooling and halved after each upsampling. Finally, following the UNet architecture, we use skip-connections across the contracting and expanding paths.

The denoiser uses a similar architecture, but with a slightly lighter design: it only has one convolution per level, and keeps a constant number of 16 features. Additionally, we suppress the skip connections between the top two levels, in order to find a compromise between UNets, where top-level skip-connections can potentially reintroduce erroneous features at late stages of the network; and auto-encoders, with excessive bottleneck-induced smoothness.

Finally, the architecture of the regression network is the same as the encoder of the segmentation modules, except that convolutions now use a kernel size of 5$\times$5$\times$5 as in~\cite{wang_deep_2020}, which greatly improved the results on the validation dataset. Regression scores are then retrieved by appending two more convolutions of 10 features (one for each QC region of interest) and a global max-pooling.

\paragraph{Learning.}
The three segmenters and the denoiser are trained to minimise the average soft Dice loss~\cite{milletari_v-net_2016}. If $Y_k$ represents the soft prediction for label $k \in {1, ...,K}$, and $T_k$ is its associated ground truth, this loss is given by:
\begin{equation}
\label{eq:soft dice}
    \text{Loss}_\text{seg} = 1 - \frac{1}{K}\sum_{k=1}^{K} \frac{2 \times \sum_{x,y,z} Y_k(x,y,z) T_k(x,y,z)}{\sum_{x,y,z} Y_k(x,y,z)^2 + T_k(x,y,z)^2}.
\end{equation}
On the other hand, the regressor is trained using a sum of square loss function. All networks are trained separately with the Adam optimiser~\cite{kingma_adam_2017} using a learning rate of $10^{-5}$. We train each module until convergence, which approximately takes 300,000 steps for the segmenters (seven days on a Nvidia RTX6000), and 50,000 steps for the regressor (one day on the same GPU). In practice, each network is concatenated to its corresponding generative model (or data augmentation model in the case of $D$ and $R$) to form a single model, which is entirely implemented on the GPU in Keras~\cite{chollet_keras_2015} with a Tensorflow backend~\cite{abadi_tensorflow_2016}.

\paragraph{Inference.}
Test scans are automatically resampled to \SI{1}{\milli\meter} isotropic resolution, and their intensities are normalised between 0 and 1. The trained model then predicts soft probabilistic segmentations for all target labels. Finally, hard segmentations are obtained by applying an argmax operation on these soft predictions. Overall, the whole inference pipeline takes between 12 and 16 seconds per scan on a RTX6000 GPU.

\paragraph{Individual volumes and ICV estimation.}
Volumes of individual brain regions are estimated by summing all the values of the corresponding soft predictions, and multiplying the result by the volume of a voxel (\SI{1}{\milli\meter}$^3$). We emphasise that summing soft probabilities rather than hard segmentation enables us to account for segmentation uncertainties and, to a certain extent, for partial volume effects~\cite{van_leemput_unifying_2003}. In turn, ICVs are estimated for every subject by summing the predicted volumes of all structures, including the intracranial cerebro-spinal fluid.

\paragraph{Dice scores.}
Quantitative evaluations of the results are performed using a hard Dice score metric, which measures the overlap between the same regions across two hard segmentations. If $X$ and $Y$ are corresponding structures in two different segmentations, their hard Dice score is given by:
\begin{equation}
    Dice(X,Y)  = 2 \times \frac{|X \cap Y|}{|X| + |Y|},
\end{equation}
where $|\cdot|$ represents the cardinality of a set. Therefore, Dice scores vary between 0 (no overlap) and 1 (perfect matching).

\paragraph{Cohen's d.}
In this work, effect sizes in hippocampal volume between control and AD populations are measured with Cohen's d~\cite{cohen_statistical_1988}. If $\mu_{C}$, $s_{C}^{2}$ and $\mu_{AD}$, $s_{AD}^{2}$ designate the sample means and variances of two volume populations of size $n_{C}$ and $n_{AD}$, where $C$ stands for Controls, Cohen's d is given by:
\begin{equation}
d = \frac{|\mu_{C}-\mu_{AD}|}{s}, \quad s = \sqrt{\frac{(n_{C}-1)s_{C}^{2} + (n_{AD}-1)s_{AD}^{2}}{n_{C}+n_{AD}-2}}.
\end{equation}
Cohen's d below 0.2 are considered to be small, whereas values above 0.8 indicate large effect sizes~\cite{cohen_statistical_1988}.

\paragraph{Regression model for ageing.}
Our ageing model includes: B-splines with 10 equally spaced knots for age, linear terms for slice spacing in each acquisition direction (i.e., sagittal, coronal, and axial), and a bias for gender. We then fit this model numerically by minimising the sum of squares of the residuals with the L-BFGS-B method~\cite{byrd_limited_1995}.

\section*{Data availability}

The code is available at https://github.com/BBillot/SynthSeg, and the trained models for \textit{SynthSeg{$^+$}} and \textit{SynthSeg} are distributed with the publicly available neuroimaging package FreeSurfer~\cite{fischl_freesurfer_2012}. The ADNI~\cite{jack_alzheimers_2008}, HCP~\cite{van_essen_human_2012}, and OASIS~\cite{marcus_open_2007} scans used in this study are all taken from publicly available datasets.

\nolinenumbers

redpajama_set_name: RedPajamaArXiv


text:
Here's What Nicola Coughlan Has Said about Her Weight Loss and Body-Shaming Comments
Nicola Coughlan is a talented Irish actress who is well-known for her portrayal of Clare Devlin in Derry Girls (2018-19) and Penelope Featherington in the Netflix series Bridgerton (2020-present).
Although she has garnered fame for her acting performances, she has also received quite the attention due to her weight. Coughlan, who weighs around 68 kg (150 lbs), has constantly received body-shaming comments, either from the critics or from the public.
The actress has mainly faced such comments during some of her interviews or press junket while promoting her movies and TV shows. Despite all of those comments, she has managed to dodge them and focus on her work only.
However, there was one instance that provoked her to give a fitting reply. It was on March 1, 2021, when the actress showed up for the Golden Globes virtual red carpet donning an amazing pastel yellow Molly Goddard gown, which was completed with a Ply Knits cropped black cardigan and De Beers jewelry — she had posted the pictures on Instagram and Twitter, too.
A post shared by Nicola Coughlan (@nicolacoughlan)
Though she looked amazing in the dress, it didn't stop one Twitter user from body-shaming her as they wrote, "The fat girl from Bridgerton is wearing a black cardigan at the Golden Globes. bc no matter how hot and stylish you are, if you're a fat girl there will always be a black cardigan you think about wearing, then decide against, but ultimately wear bc you feel like you have to."
Later, when that Twitter user faced backlash, they went on to defend their use of "fat" as a "descriptor," and identify themselves as a "fat person". When Coughlan noticed that comment, she gave a befitting reply as she wrote,
"I thought the cardigan looked ace. Molly Goddard used them on her runway with the dresses that's where the idea came from, also I have a name."
On the next day, March 2, the 5 feet 1-inch tall actress went on to write a series of tweets where she slammed those who frequently questioned her weight. Through those tweets, she mainly asked people to "judge actors for their work and not their bodies."
Her first tweet was an article from The Guardian that she wrote around two years ago. "Hello! So I wrote this two years ago for the Guardian but I just thought I'd share it again. TLDR: Can we judge actors for their work and not their bodies," Coughlan wrote.
https://twitter.com/nicolacoughlan/status/1366503990138195972
In the next one, she requested people to stop asking women about their weight during interviews, as it is completely irrelevant. She also added that she has been "seeing a lot of interviews from 10 years ago where people go "Oh weren't the questions so inappropriate!" unfortunately it's still happening."
In the third follow-up tweet, she stated that when she is asked about her body in interviews, she feels deeply uncomfortable and sad that she is just not allowed to talk about the job she does and loves.
In the next one, she wrote, "It's so reductive to women when we're making great strides for diversity in the arts, but questions like that just pull us backward."
Then, she went on to say, in the nicest way possible, that she is not a body positivity activist — she is an actor and she would lose or gain weight if a role requires. "My body is the tool I use to tell stories, not what I define myself by," she added.
In the second last tweet, she mentioned that it's 2021 and "it would be nice if we didn't have to keep having this conversation." She further added that she would love it if she never get asked about this topic again during interviews.
In the end, she again put up her article from The Guardian and wrote, "Anyways, I put all my thoughts in what I wrote two years ago and that's all I've ever wanted to say on it so have a read if you like."
The article she wrote in The Guardian in 2018 was a reply to a critic who called Coughlan an "overweight little girl" in his review of the actress's performance in "The Prime of Miss Jean Brodie."
In her piece, the Irish actress had fired back with an essay in The Guardian, where she slammed the industry's focus on women's bodies. She had written;
"I hope in the future that more people will talk about our work, our inspirations, our drive, rather than our looks. A revolution is happening, and I want to play my part in it."
Other than these, there have been several other occasions in which she has freely spoken on her weight and has given a smooth reply to them.
Coughlan's comments still remain there, but, there are also some question which asks if she has done any weight loss to fully silence those body-shaming commenters. Well, currently there have been no reports of the actress doing weight loss.
And, as we have seen in her above-mentioned tweets, it can be believed that she is happy with her body and she doesn't need to lose any weight unless a role requires it.
Related Topics:BridgertonNicola CoughlanWeight Loss
Is Samantha Sloyan Married? Inside Her Dating Life and Gay Rumors
Erica Cerra Shares Two Kids with Her Husband — Inside 'The 100' Star's a Decade Long Married Life
Jonathan Bailey Is Secretive of Partner and Dating Life — Here Is an Insight
Did 'The Last Kingdom' Star Eliza Butterworth Went Through Weight Loss? Find Out


Dumpster rental services don't have to be a headache. It all depends on who you hire in Bend, OR. Let Dumpster Deliveries help you. Call us at 888-234-4587. When regular garbage services aren't working, then it's time for you to think about taking things to the next level.
Renting a dumpster can considerably enhance your business by containing garbage in a clean, organized fashion. Renting a temporary dumpster is ideal for bigger household garbage removal projects. There's no need to pile it all in your automobile. We have small rentals starting at great prices!
Dumpster Deliveries is the best corporation for you to rent a dumpster from in Bend, OR.
You cannot make a mistake when you use Dumpster Deliveries. There is not a garbage removal job out there that we don't have the resources to take care of. All of our junk removal costs are included so you do not need to worry about hidden service fees when you get your bill. Also, our customer support is excellent, so you'll never be on your own or forgotten when you go through Dumpster Deliveries.
If you or your friends have to transport your own garbage, you may be making some big mistakes. First, there is more legal responsibility that you incur when you dispose of your own junk. The second issue is that it requires a great deal of time and money to transport your own garbage. Third, you might be improperly getting rid of waste materials. Whenever you hire experts to manage your junk removal, all those problems disappear.
The dumpster rental procedure is very easy when you work with a professional agency. First, find out what size dumpster you need. The dumpster rental agency you work with can help you figure out what you need. It all depends on what renovation project you need it for and how much waste you are producing. Then, you contact an organization, receive your estimate, make your transaction, and wait to obtain your dumpster. As a client, all you need is satisfaction. Dumpster Deliveries in Bend can give you just that. Contact us at 888-234-4587 for a cost-free estimate.
A roll off dumpster is one that will be rolled off a delivery truck at your residence. The container looks like a big box with a big, open top. When you're thinking about a sizeable job or renovation, you can reserve these dumpsters in Bend OR. The most widespread reason they are used is to dispose of debris following a construction or rehabilitation job. Should you require a roll off in Bend, you should call 888-234-4587 for more information.
There are lots of factors to be aware of if you are reserving a roll off dumpster in Oregon. Take the time to find out more before you get started. Since each job and rental differs, you should not assume that you already know every single thing you will need for this job. As soon as you make arrangements for your dumpster, you are going to need to choose a site to set it. All you need to do is load it with debris, and then arrange to get it picked up when it is full.
Ordering a dumpster in Bend, OR is always a trouble-free procedure with Dumpster Deliveries.
The optimal size for your task is obtainable.
There are not any extra fees for drop off and pickup of the waste container.
It ought to be easy to rent a roll off in Oregon. Choosing a company that you are able to trust is also important. This means a flawless, worry-free job each time. You need to have confidence that you'll be able to complete your job without having any problems about your dumpster services.
If you're considering renting a roll off dumpster in Oregon, there are some specifics to consider first.
The date you might want the dumpster delivered and the amount of time you are likely to require it.
The placement of the roll off is particularly important. An area with gravel, cement, or pavement is always the best option. Avoid setting it on dirt. The rollers on the dumpsters may harm even these areas, so you might need to lay a large section of wood where you'd like it to be placed.
There might be permits required to order a unit so it's a good idea to visit Bend representatives first. This is especially important if you are putting it on public premises. Most municipalities don't require permits if it will be placed on private property.
With just a little more information, you'll be ready to reserve a roll off dumpster in Bend.
First off, you'll need to figure out the optimal size to suit your requirements and the amount of time the task requires.
Attempt to estimate these as closely as possible, seeing that undervaluing either factor could cost you a lot of cash. It's best to have some extra time and room than not enough. The date you pick for your unit's drop off may influence the progress of your project so take this into account.
If you happen to be new to dumpster rentals in Bend, it is common that you will have many more concerns about how to go about obtaining one for your job. Repeat clients often have questions regarding their specific job as well. Dumpster Deliveries can provide you with all of the information you will want. Just call 888-234-4587.


High Quality Content by WIKIPEDIA articles! NCSA HTTPd was a web server originally developed at the NCSA by Robert McCool and others. It was among the earliest web servers developed, following Tim Berners-Lee's CERN Tony Sanders' Plexus server, and some others. It was for some time the natural counterpart to the Mosaic web browser in the client-server World Wide Web. It also introduced the Common Gateway Interface, allowing for the creation of dynamic websites. When development slowed down, an independent effort, Apache project, took the codebase and continued; meanwhile, NCSA released one more version (1.5), then ceased development. At the time, NCSA HTTPd powered over 95% of all webservers on the Internet; nearly all of them switched over to Apache.


text:
Limited transformer for ECMAScript 2015 modules (AMD)

Converts this code:
```js
import x from '/path/to/x';
import y from '/path/to/y';
doSomething();
export default x + y;
```

Into this one:
```js
define(['/path/to/x', '/path/to/y'], function (x, y) {
  doSomething();
  return x + y;
});
```

Instead of this one (generated with ``babel-plugin-transform-es2015-modules-amd``):
```js
define(['exports', '/path/to/x', '/path/to/y'], function (exports, _x, _y) {
  Object.defineProperty(exports, "__esModule", {
    value: true
  });

  var _x2 = _interopRequireDefault(_x);

  var _y2 = _interopRequireDefault(_y);

  function _interopRequireDefault(obj) {
    return obj && obj.__esModule ? obj : {
      'default': obj
    };
  }

  doSomething();
  exports.default = _x2.default + _y2.default;
});
```

Supported features:
- ``import SPECIFIER from 'PATH'``
- ``import 'PATH'``
- ``import {SPECIFIER1, SPECIFIER2 as SPECIFIER3} from 'PATH'``
- ``export default NODE``

Other features aren't supported.

**Warning**. If no ``import`` or ``export`` are presented in JavaScript file, the plugin does nothing (means it doesn't wrap code with ``define``).

## Installation

```sh
$ npm install --save-dev babel-plugin-transform-es2015-modules-simple-amd
```

## Usage

### Via `.babelrc` (Recommended)

**.babelrc**

```json
{
  "plugins": ["transform-es2015-modules-simple-amd"]
}
```

### Via Node API

```javascript
require('babel').transform('code', {
  plugins: ['transform-es2015-modules-simple-amd']
});
```

[The same thing for CommonJS](https://github.com/finom/babel-plugin-transform-es2015-modules-simple-commonjs).

Thanks to [RReverser](https://github.com/RReverser/babel-plugin-hello-world).

redpajama_set_name: RedPajamaGithub