The film follows the passionate hunters Henrik Lott, Kai-Uwe and Hagen Denker while tracking kudu in the Erongo Mountains of Namibia. Elusive animals, kudu are also called "grey ghosts of the mountain" in their native lands. Their habitat is rough and wild, and stalking the animals is physically demanding. But it rewards the hunter with an unforgettable experience, breathtaking landscapes and the profound insight that hunting is the purest and most honest form of our existence, and today presents the only way to protect areas against the destruction wrought by civilization.
Hunting serves to protect and preserve nature.
Sebastian Steinbrink-Minami's film crew followed the hunters for twelve days on their adventure through the Erongo Mountains in Namibia to shoot "Erongo – The grey ghost of the mountain". The region is well-protected against the ravaging effects of human civilization, and is home to several exotic species such as black rhinos, black nose impalas, Hartlaub's francolin, leopards, dik-diks, Hartmann's mountain zebra and many species of wildfowl.
The full-length feature will be released on the Krieghoff YouTube channel in fall 2016.
Embarking on the adventure of an African hunt requires the right combination of weapon and ammunition when stalking dangerous wild game. In the Krieghoff Classic "Big Five" double rifle, the richly traditional company offers impressive engineering in a classic style for the large calibers used to hunt big game. The "Big Five" has all the attributes of the legendary safari rifles from the golden era of African hunting. Visit all4hunters.de for more information on the Krieghoff Classic Double Rifle.
Krieghoff offers the drillings in the Optima and Trumpf series in their classic form and as a double rifle drilling for hunting scenarios. The Optima 20 is available as a "Big Five" double rifle drilling in the big game calibers up to .500/.416 N.E.
The Krieghoff Hubertus single shot break action rifle is light, short and wonderfully responsive. Kings and poachers alike once appreciated the benefits of a single shot rifle. Perhaps it is the restriction to a single shot that emphasizes the seriousness of hunting. The first and only shot has to take down the game and bag the coveted trophy.
There are many different sides and facets to hunting. Configured as an over-and-under combination rifle/shotgun, a two-caliber double rifle combination or an over-and-under double rifle, the Krieghoff Ultra 20 can turn any hunting opportunity into a success. Its slender shape makes the Ultra 20 particularly responsive and precise.
You will find additional information and an overview of Krieghoff's hunting weapons on the Krieghoff website.
Browse the current 2016 Krieghoff catalog here at all4shooters.com.
Watch more hunting films at www.hunting.film.


text:
Programmes & Initiatives
International Women's Day – Carmel McKinney
Home / Membership / Success Stories / International Women's Day – Carmel McKinney
Carmel McKinney OBE was formerly chairperson of two public-sector bodies within the education sector and, in June 2015, was appointed as the first female chairperson of the Northern Ireland Fire & Rescue Service.
In 2017 she won the Award for Outstanding Management and Leadership at the Women in Business Awards, was runner up in The Guardian Public Services Awards for Leadership Excellence, and (most recently) was appointed onto the Guardian Public Services Advisory Board. She is a key role-model for all females wanting to reach their full potential as leaders in the Boardroom setting and has given outstanding leadership in her drive to mentor and encourage other females to consider positions within Boards. She has actively promoted the achievements of women in public life, having been the keynote speaker and panelist at numerous events, as well as interfacing with Fire and Rescue Services across the UK and in the United States as an ambassador for NIFRS.
Do you have a female role model who has inspired you in your career?
I don't feel, as a woman, that we need to automatically say someone famous. My role model has been my late mother, Catherine.
Mum was a high-achieving woman in a time when very few females held senior positions in organisations. What she always instilled in her 2 daughters was that we were well able to be successful in our chosen fields if we worked hard, treated people with respect and were professionally humble. Strong emotional intelligence was her greatest gift, and her capacity to encourage yet constructively challenge for our own good was the greatest inspiration for anyone to have. At her funeral, hundreds of people arrived to pay their respects and to tell how she inspired them in their careers, so why would I look for a celebrity when I had such an incredible role model in my mother.
What has been your defining career moment?
I have had many successes in my career, and I am enormously privileged to have achieved so much. For me, that defining career moment had to be when I received by OBE at Buckingham Palace. I was with my mum, dad and sister, Sheena. It was the family's achievement as, without them, I could never have done all the things I have achieved. As my name was called out to move forward to receive my award, I thought – wow – I am an ordinary person who has done extraordinary things. What's not to like about that!!
What advice would you give your 18-year-old self (or any young woman reading this)?
Think I would say self-belief and to seek a mentor to help you build your chosen pathway; be willing to take the knocks along the way – learn from them, and try again. Avoid negative people, as they drain enthusiasm and confidence; seek out the radiators – people who want you to do well and who are happy to help you get there.
Tell us one surprising fact about yourself.
I am a highly accomplished, award-winning champion Irish Dancer and have adjudicated competitions nationally and internationally. My claim to fame was the invitation to dance for the President of the USA at the St Patrick's Day celebrations in the White House.
What's next for you in your career?
Continue to fulfil my role as Chairperson of NIFRS to the best of my ability; that will keep me busy for now!
International Women's Day – Barbara Gray
International Women's Day – Julie Jamison
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Cask Ale Week is well and truly under way, running from now until 6 October, with a sprightly scattering of events up and down the land.
While there's nowt wrong with good beer in a bottle, the chance to taste well-kept cask ale is a real treat. But not everyone has had the chance to try it. Some are put off by the image – but it's not all beards and pint glasses (nowt wrong with either of them, either, but you get my drift), as the posse of glamorous female beer sommeliers illustrate so well.
That's why this year's theme is The Great Big Taste Challenge. Invite anyone you know to take part in any of the events taking place. I've listed a few highlights here, but for hundreds of other events, head to the website http://www.caskaleweek.co.uk/, look up @caskaleweek on twitter or check out the facebook page.
London brewer Truman's is making a comeback - 24 years after closing the doors of its iconic East End brewery, writes Julie Sheppard.
Located in Hackney Wick, a short distance from its original home on Brick Lane, Truman's new brewery, The Eyrie, is the dream come true of founders Michael-George Hemus and MD James Morgan – seen here with guest eagle.
"There was a sense that something was lost in the community when Truman's closed in 1989, so it's great to be back in the East End and brewing at a time when lots of exciting things are happening in this area," said MD James. 'The London brewing scene is very open – which is so different to other countries – and it's great to be a part of it!" added head brewer Benedikt Ott.
Everyone loves a christening, but coming up with the chosen moniker isn't always that easy. Jim Crews rose to the challenge with Thame'in of the Brew, the name for the ale created by Vale Brewery in Brill for this year's Thame Food Festival on 28 September. With its English hops and malted barley, the beer has a "traditional harvest festival feel". Jim pulled the first pint at the brewery's Open Day on 7 September, but you can sample it at Vale Brewery's stall on 28 September.
Other breweries present on Festival day will include regional stalwart Chiltern and malty funsters, XT Brew, who recently announced that the Royal British Legion have commissioned them to brew a special beer for its bars. Available in October and November, all profits will go to the Poppy Appeal.
Once Upon a Tree, the boutique cider producer named Best Drinks Producer in the 2012 BBC Food & Farming Awards, will be moving into wine making from the 2013 vintage.
A wine consultant since before he began making cider, Simon Day will be expanding production operations in Putley, Herefordshire, to vinify wine as well as his award-winning ciders. Bringing his winemaking skills into the cider house with him, Simon has long felt that cider – and perry – deserves a spot at the dinner table. Now there will be a vinous counterpart with his name alongside the fruits of the orchard.


One of the biggest assets of our facility is our recording space. At over 700 square feet the room has an open, natural sound and is dead quiet. Last year shows recorded here received a total of 5 Canadian Screen Award nominations.
Filming and recording music has always been a passion of ours, and Up is Loud has had many opportunities to capture great musical performances, live.
These videos showcase both established and emerging artists performing in every setting imaginable. From the blistering sidewalks of Toronto to beautiful churches and tiny apartments, the Up is Loud team produces engaging profiles of remarkably talented musicians.
Up is Loud has had the good fortune to work on a vast array post audio projects over the last three decades. Award winning doc series, action features, TV dramas, animation; too many shows to list!
We are continuously developing and pitching new content ideas. From music to lifestyle, reality and documentary, we've got fresh shows in every genre.


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Q: matplotlib.legend - `ncols` not recognized - version compatibility problem? I have a python script using matplotlib and the function legend to which I pass the option ncols. In itself, it is not a problem and run very well on my computer but it does not work for my colleagues who get the following error:
TypeError: __init__() got an unexpected keyword argument 'ncols'

I noticed that I am using Matplotlib version 3.6.2 while my colleagues are using Matplotlib version 3.4.2.
Could someone tell me if the option ncols was added or changed made to it in between Matplotlib version 3.4.2 and 3.6.2?
I could not find this information in Matplotlib release notes but I noticed on the matplotlib.legend documentation the following:
"For backward compatibility, the spelling ncol is also supported but it is discouraged. If both are given, ncols takes precedence."
It makes me think that my colleagues impossibility to run the code might be linked to some ncols update.

A: You basically found it. If you look on the API for 3.4 and 3.5, the parameter was ncol with no s. The note in 3.6 (Stable) means that it still supports ncol but will prefer and overwrite if you use ncols. If you want to maintain usage across you and your colleague's different Matplotlibs, just use ncol.

redpajama_set_name: RedPajamaStackExchange


Quantify your scouting efforts and increase your quality.
Profile 90 is a smart scouting platform that integrates scientific insights taking traditional scouting to a new level.
It covers the four key cornerstones of scouting; Physical, Tactical/Technical, Psychological and Social, in the most objective way possible.
Profile 90 is the first platform to give you a 360 view of a player, before you sign them.


text:
Q: Update UI after calling external C++ library from C# I'm building a C# wrapper class library for a well known software in robotics simulation named V-REP. The API works well but I confront strange behavior. After I get joint feedback from the program using an external library supplied by software, UI is not updated until sub routine (having while loop) is finished. In other words, it only displays last value although I can log all values on output window. I even made large time delay, still no luck. I used the dispatcher technique to update the UI, still not working.
private void SetJointPosition(object sender, RoutedEventArgs e)
        {
            MoveJointsButton.IsEnabled = false;
            // Get the vrep handle of each position
            Vrep.simxGetObjectHandle(ClientId, "PhantomXPincher_joint1", out JointHandles[0], simx_opmode.oneshot_wait);
            Vrep.simxGetObjectHandle(ClientId, "PhantomXPincher_joint2", out JointHandles[1], simx_opmode.oneshot_wait);
            Vrep.simxGetObjectHandle(ClientId, "PhantomXPincher_joint3", out JointHandles[2], simx_opmode.oneshot_wait);
            Vrep.simxGetObjectHandle(ClientId, "PhantomXPincher_joint4", out JointHandles[3], simx_opmode.oneshot_wait);

            ////Assign Target positions
            TargetJointPosition[0] = (float)(JointBase.Value * (Math.PI / 180.0));
            TargetJointPosition[1] = (float)(JointShoulder.Value * (Math.PI / 180.0));
            TargetJointPosition[2] = (float)(JointElbow.Value * (Math.PI / 180.0));
            TargetJointPosition[3] = (float)(JointWrist.Value * (Math.PI / 180.0));

            //Set the position of each joint
            Vrep.SimSetJointTargetPosition(ClientId, JointHandles[0], TargetJointPosition[0]);
            Vrep.SimSetJointTargetPosition(ClientId, JointHandles[1], TargetJointPosition[1]);
            Vrep.SimSetJointTargetPosition(ClientId, JointHandles[2], TargetJointPosition[2]);
            Vrep.SimSetJointTargetPosition(ClientId, JointHandles[3], TargetJointPosition[3]);

            //Read Joint Position (start streaming)
            Vrep.SimGetJointPositionInit(ClientId, JointHandles[0]);
            Vrep.SimGetJointPositionInit(ClientId, JointHandles[1]);
            Vrep.SimGetJointPositionInit(ClientId, JointHandles[2]);
            Vrep.SimGetJointPositionInit(ClientId, JointHandles[3]);

            while ((Math.Abs(ActualJointPosition[0] - TargetJointPosition[0]) > 0.1) ||
                   (Math.Abs(ActualJointPosition[1] - TargetJointPosition[1]) > 0.1) ||
                   (Math.Abs(ActualJointPosition[2] - TargetJointPosition[2]) > 0.1) ||
                   (Math.Abs(ActualJointPosition[3] - TargetJointPosition[3]) > 0.1))
            {
                // Get current angle of each joint
                ActualJointPosition[0]=  Vrep.SimGetJointPositionRadian(ClientId, JointHandles[0]);
                ActualJointPosition[1] = Vrep.SimGetJointPositionRadian(ClientId, JointHandles[1]);
                ActualJointPosition[2] = Vrep.SimGetJointPositionRadian(ClientId, JointHandles[2]);
                ActualJointPosition[3] = Vrep.SimGetJointPositionRadian(ClientId, JointHandles[3]);

                //Update guages
                GBase.Dispatcher.Invoke(()=>
                {
                    GBase.Scales[0].Needles[0].Value = Math.Round(ActualJointPosition[0] * (180.0 / Math.PI));
                    Debug.WriteLine(DateTime.Now + " " + GBase.Scales[0].Needles[0].Value);
                }) ;

                GShoulder.Dispatcher.Invoke(() =>
                {
                    GShoulder.Scales[0].Needles[0].Value = ActualJointPosition[1] * (180.0 / Math.PI);
                });

                GElbow.Dispatcher.Invoke(() =>
                {
                    GElbow.Scales[0].Needles[0].Value = ActualJointPosition[2] * (180.0 / Math.PI);
                });

                GWrist.Dispatcher.Invoke(() =>
                {
                    GWrist.Scales[0].Needles[0].Value = ActualJointPosition[3] * (180.0 / Math.PI);
                });

            }

            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[0]);
            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[1]);
            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[2]);
            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[3]);

            MoveJointsButton.IsEnabled = true;

        }}
}

As you may see, I log joint position with no problem on the output window.

I use a WPF application.

A: This is because the while loop is being executed on the UI thread. You can't run a loop on and the update the UI on the same thread simultaneously.
That's why you should always perform any long-running work on a background thread and update UI at regular intervals using the dispatcher. Please refer to my answer here for more information about this:
How to create percentage of loop(processing) [c#]
You could try to execute while loop in a task, e.g:
Task.Run(() =>
        {
            while (...)
            {
                //...

                //Update guages
                GBase.Dispatcher.Invoke(() =>
                {
                    GBase.Scales[0].Needles[0].Value = Math.Round(ActualJointPosition[0] * (180.0 / Math.PI));
                    Debug.WriteLine(DateTime.Now + " " + GBase.Scales[0].Needles[0].Value);
                });
                //..
            }
        }).ContinueWith(
        (t) =>
        {
            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[0]);
            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[1]);
            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[2]);
            Vrep.SimGetJointPositionEnd(ClientId, JointHandles[3]);

            MoveJointsButton.IsEnabled = true;
        }, CancellationToken.None, TaskCreationOptions.None, TaskScheduler.FromCurrentSynchronizationContext());

Make sure that you marshal any code that access a UI control inside the task back to the UI thread using the dispatcher as controls can only be accessed on the thread on which they were originally created, i.e. the UI thread.


text:
Rebecca Conroy
Dating an Economist
[A series of performative dinner dates and audio recordings]
During 2015 Rebecca Conroy went on a series of dates with economic philosophers and finance dudes across North America and Europe and called it an art project. The result is a series of recorded conversations and thoughts on the subject of finance capitalism, power, intimacy, and the intangibility of how we create value. Part radio documentary, part experimental fiction, this work is ongoing and will appear finally as a four-part experimental audio documentary in 2016. First audio edition commissioned by Radio National (Creative Audio Unit) in June, 2016.
The intention of this work is to provoke and ponder the relationship between capital and the artists labouring body, different imaginings of currency, and to explore the poetry of finance algorithms and acts of self-determination for a possible post-capitalist future.
Follow the dates here
Listen to a radio interview about the project here
Hear the commissioned radio piece here
extraction sociopathsgig economysupply chain to my heartthe artists labouring bodythe neoliberal artist performing its services for the "public good""creative" economy
This project has been assisted by the Australian Government through the Australia Council for the Arts, its arts funding and advisory body.


Bleach Typos and other minor things.
Thanks for your bleach releases I am currently enjoying them on a re-run through.
Every now again I come across a minor error (typo) slight timing etc.
Do you guys want to know about them for stuff already released?
08:09 "in the past for sewing carrots" I assume that want to be "in the past for serving carrots"
19:27 "She's being forced many someone she's never met!"->"She's being forced marry someone she's never met!"
I am continuing to watch let me know if you would like to continue to mention any others as I go on.
"As if I'll let you...." + "have your wail!"
"As if I'll let you...." + "have your way!"
"I will gamer all my strength to counter you"
"I will gather all my strength to counter you"
"Did something happen to hen'?"
"Did something happen to her?"
Thanks for posting the errors. But i doubt we will be posting any fixes for these right now. Though anything is possible.


Nokia Lumia 900 is a smartphone for fans of new technologies. This model gives you unlimited use of high-speed Internet. Its users will experience incredible speed thanks to a powerful Snapdragon processor with a frequency of 1.4 GHz. Moreover, the Nokia Lumia 900 is ideal for sharing your experiences on popular social networking sites such as Facebook and Twitter, and posts can instantly enrich the photos taken with the integrated camera 8Mpx with Carl Zeiss technology. Unique large ClearBlack AMOLED touchscreen with a diagonal 4.3-inch layer of protective glass works not for watching movies and photos, even in bright sunlight. The model is also equipped with two cameras, so you can conduct video calls and also to share with the caller view around us. Nokia Lumia 900 is also interesting 100 000 applications available on the Marketplace. This model is the perfect companion for every day, and on the go. Nokia Maps will we navigate to your destination even in offline mode, and the size and quality of the screen make the Nokia Lumia 900 easily replaces traditional navigation. Nokia Music with Mix Radio app - allowing free play music with finished or customized to individual preferences music channels. Nokia Music also has a built-in application Concerts, with which you can search for music events in the area.
Remember that entered PIN on the website must be exactly the same as PIN entered in the setup program on your Nokia Lumia 900.


It's what I do! It's who I am!
I strongly recommend otherwise. Sullinger could still be traded, and then it'd be just plain awkward.
Yeah, that would be pretty lame.
A couple of years ago I got my Rondo jersey right in the middle of a ton of trade rumors involving him. I made that purchase with one eye shut, lol.
our Achilles heel is rebounding what Sully does best, it's one of the reasons were doing better lately. Sully is young improving defensively and believe it or not helps KG out a ton as well. I don't think he's going anywhere.
I "m wanting an Bradley jersey.... he is my favorite player after KG...I had his tee shirt on for this win...when ever I wear it the Celtics win.
I don't Danny has the nads to trade away AB. Not after he has picked up this team.


More Hours in the Day: With an au pair, you'll gain an extra set of hands in the morning before starting your own day. Since au pairs work flexible schedules, they can help get your kids ready in the morning, and then pick up your school age children in the afternoons while also caring for younger children. You might even be able to fit in a weekly date night again!
Back to School Time: Au pairs can help with school pick-ups and drop-offs and can be an invaluable resource when it comes to homework help, test preparation, and school projects! Not to mention that language learning is easiest in younger brains – exposure to a non-English speaking au pair may help lay the foundation for your child's second (or third) language-learning down the line.
Divide and Conquer: Kids' schedules can be busy year-round between soccer practice, music lessons, dance class, etc., but the Fall can seem a little busier than other seasons! Imagine not having to scramble to leave work in order to get the kids to practice on time or no longer sacrificing one activity because another child's schedule conflicts. Rely on your au pair to help you be in two places at once!


Here's the weather report for Barbados, Saturday, February 16, 2019.
Today is expected to be fair to partly cloudy and breezy with a few brief showers.
Winds are from the E - ESE at 20 to 40 km/h for the first half of the day. Tonight winds are predicted to be generally from the East at 20 to 40 km/h.
The sun rose at 6:21 am and will set at 6:04 pm this evening.
Throughout today, the temperature is predicted to reach a high of 29.7⁰C during the morning and drop to a minimum temperature of 25⁰C tonight.
Seabathers and seacraft operators can expect Moderate in open water with swells from 2.0m to 2.5m.
Small craft operators and seabathers should exercise caution.


Citipati, for The Teaching Company, by Karen Carr and Karen Carr Studio, Inc.
Fossil specimens of Citipati found in Mongolia provide a fascinating look into the lives of ancient animals: Several fossil individuals have been found preserved as they sat on their nests, brooding their eggs. This evidence of nesting is one of the many links connecting modern birds with dinosaurs.
Although the family name for Citipati, Oviraptoridae, means "egg thief," it is possible oviraptors have had a bad rap, at least in part: Early finds of oviraptor fossils were near fossilized eggs, and the 'raptors were thought to be egg thieves. It is possible they were, but it is also possible the eggs they were found near were their own.


text:
Learning Deformable Registration of Medical Images with Anatomical Constraints
01/20/2020 ∙ by Lucas Mansilla, et al. ∙ 13 ∙ share
Deformable image registration is a fundamental problem in the field of medical image analysis. During the last years, we have witnessed the advent of deep learning-based image registration methods which achieve state-of-the-art performance, and drastically reduce the required computational time. However, little work has been done regarding how can we encourage our models to produce not only accurate, but also anatomically plausible results, which is still an open question in the field. In this work, we argue that incorporating anatomical priors in the form of global constraints into the learning process of these models, will further improve their performance and boost the realism of the warped images after registration. We learn global non-linear representations of image anatomy using segmentation masks, and employ them to constraint the registration process. The proposed AC-RegNet architecture is evaluated in the context of chest X-ray image registration using three different datasets, where the high anatomical variability makes the task extremely challenging. Our experiments show that the proposed anatomically constrained registration model produces more realistic and accurate results than state-of-the-art methods, demonstrating the potential of this approach.
READ FULL TEXT VIEW PDF
Lucas Mansilla
Diego H. Milone
Enzo Ferrante
FDRN: A Fast Deformable Registration Network for Medical Images
Deformable image registration is a fundamental task in medical imaging. ...
11/04/2020 ∙ by Kaicong Sun, et al. ∙ 0 ∙ share
An Unsupervised Learning Model for Deformable Medical Image Registration
We present an efficient learning-based algorithm for deformable, pairwis...
02/07/2018 ∙ by Guha Balakrishnan, et al. ∙ 1 ∙ share
Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration
Deformable registration has been one of the pillars of biomedical image ...
09/24/2018 ∙ by Enzo Ferrante, et al. ∙ 2 ∙ share
Convolutional Neural Networks vs. Deformable Image Registration For Medical Slice Interpolation
Medical image slice interpolation is an active field of research. The me...
04/28/2020 ∙ by Dilip Kumar Verma, et al. ∙ 0 ∙ share
Elastic Registration of Geodesic Vascular Graphs
Vascular graphs can embed a number of high-level features, from morpholo...
09/14/2018 ∙ by Stefano Moriconi, et al. ∙ 2 ∙ share
VoteNet++: Registration Refinement for Multi-Atlas Segmentation
Multi-atlas segmentation (MAS) is a popular image segmentation technique...
10/26/2020 ∙ by Zhipeng Ding, et al. ∙ 0 ∙ share
Fast Graph-Cut Based Optimization for Practical Dense Deformable Registration of Volume Images
Objective: Deformable image registration is a fundamental problem in med...
10/19/2018 ∙ by Simon Ekström, et al. ∙ 2 ∙ share
This week in AI
Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.
Deformable image registration is one of the pillar problems in the field of medical image analysis. Disease diagnosis, therapy planning, surgical and radiotherapy procedures are a few examples where image registration plays a crucial role. In the medical context, the problem consists in aligning two or more images coming from different patients, modalities, moments or view points. Such alignment is achieved by means of a deformation field, that warps the so called
source image, aligning it with the corresponding target image. Traditionally, image registration has been formulated as an optimization problem, where the objective function represents a similarity measure, which indicates how well the source image matches the target.
Recently, the latest advances in machine learning allow us to conceive image registration under an entirely different paradigm. In particular, deep convolutional neural networks (CNN) have proved to outperform all existent strategies in other fundamental tasks of computer vision, like image segmentation
(Long et al., 2015) and classification (Krizhevsky et al., 2012) . During the last years, we have witnessed the advent of deep learning-based image registration methods (Li and Fan, 2018; Yang and others, 2017; Rohé et al., 2017; Sokooti and others, 2017; de Vos et al., 2017, 2019; Balakrishnan et al., 2018; Dalca et al., 2018; Estienne et al., 2019) , which achieve state-of-the-art performance, and drastically reduce the required computational time. These works have made a fundamental contribution by setting novel architectures for CNN-based deformable image registration (following supervised, unsupervised and semi-supervised training approaches). However, little work has been done regarding how can we encourage our models to produce not only accurate, but also anatomically plausible results, which is still an open question in the image registration community.
Image registration is employed in several medical tasks, e.g. when segmenting organs at risk in radiotherapy planning (Oh and Kim, 2017) , co-registering MRI brain images prior to neuro-morphometry analysis (Gaser, 2016) or fusing pre and post-operative images to perform minimally invasive surgeries (Ferrante and Paragios, 2017) ). In these applications, producing anatomically plausible results is of paramount importance to guarantee the correctness of such medical and analytical procedures. Most of the existing registration methods impose smoothness constraints to the deformation fields or incorporate pixel-level losses into the objective functions to encourage anatomical plausibility. However, smooth deformation fields do not guarantee realistic results: in some cases, like moving organs for example, we require sharper deformations in the organ boundaries in order to preserve the anatomy. Regarding current pixel-based metrics (like Dice coefficient or Cross Entropy, for example), they do not consider the complete global context and therefore do not necessarily correlate with higher anatomical plausibility. The challenge we address is how CNN-based image registration models can produce accurate and anatomically plausible results after registration, that is, realistic results. Objectively quantifying the degree of realism of a medical image and incorporating it into the learning process of deep learning-based registration models is an extremely complex task. Here we propose to learn such measure from anatomical segmentation masks.
In this work, we argue that incorporating priors in the form of global anatomical constraints (Oktay et al., 2018) into the learning process of deep learning-based registration models, will further improve the accuracy of the results and boost the realism of the warped images after registration. We address the question about how we can incorporate anatomical priors into deep learning-based image registration methods in order to obtain more realistic results. In that sense, our contributions are four-fold: (i) we extend, for the first time, the concept of anatomically constrained neural networks (Oktay et al., 2018)
to the image registration problem, (ii) we perform a deeper study of the complementarity between global and local loss functions defined over segmentation masks, (iii) we introduce the novel anatomically constrained registration network (AC-RegNet) architecture and validate it in the challenging task of X-ray chest image registration, comparing its performance with state-of-the-art existing methods and (iv) we showcase several application scenarios for AC-RegNet in the context of X-ray chest image analysis including multi-atlas segmentation, automatic quality control and pathology classification, where anatomical plausibility is highly relevant, especially when performing pathology classification using anatomical segmentation masks. In this case, the masks are obtained through registration-based label propagation. Thus, the anatomical plausibility of the deformed segmentation masks is crucial for the classification task.
We evaluate the proposed method in the context of X-ray chest imaging using three different datasets, including an interesting study about the behaviour of the global anatomical constraints when compared with a local metric. We show that the proposed method encourages the registration models to warp images in the space of anatomically plausible solutions while, at the same time, increasing the accuracy of the results.
2 Related works
Inspired by Horn and Schunck. (1980) and Lucas and Kanade (1981) , the research communities of computer vision and medical imaging have made major efforts towards developing more accurate and efficient registration methods. Since then, deformable image registration has been modelled in multiple ways (see Sotiras et al. (2013) for a comprehensive description), most of them posing image registration as an optimization problem, which in its general form can be formulated as
^T=\operatornamewithlimitsargminTM(I∘T,J)+R(T), (1)
where I is the source (moving) image, J is the target (fixed) image, T parameterizes a spatial transformation that maps each point of the image I to J, M corresponds to the criterion of (dis)similarity that quantifies the quality of the alignment between the warped source image I∘T and the target image J, and R corresponds to the regularization term that imposes geometric constraints on the solution. In deformable image registration, the spatial transformation T is characterized by a deformation field, which represents the pixel displacements. The optimal transformation ^T aligning I with J is computed by solving this minimization problem. Classical non-learning based methods, like the one implemented in the popular toolbox SimpleElastix (Marstal et al., 2016) , which will be used as baseline in this work, compute the optimal transformation by iteratively exploring the space of potential transformation parameters. In this case, a B-spline transformation is used to parameterize the deformation field, whose parameters are adjusted by minimizing the differences between the images. The main advantage of these methods is that they do not require a training process. Therefore, they can easily adapt to unseen images, being robust enough to be used in different image modalities and organs. However, when dealing with deformable registration, these algorithms are computationally expensive due to the high dimensionality of the parameter space, making the image registration process highly time consuming.
Existing CNN-based image registration methods are usually classified as supervised or unsupervised, depending on whether or not they use ground truth deformation fields to compute the loss function during training. Inspired by the original FlowNet for vector flow estimation
(Dosovitskiy et al., 2015) , supervised CNN-based image registration methods (Yang and others, 2017; Rohé et al., 2017; Sokooti and others, 2017) posed image registration as a regression problem. Given a pair of source and target images, they aim at regressing a deformation field that matches the ground-truth. One of the advantages of these methods is its independence with respect to image modalities: given a training dataset with pairs of images and their corresponding ground-truth deformations, it learns to map images to deformation fields without using any kind of similarity measure to compare them. However, getting such good datasets is a difficult task and makes these approaches impractical.
On the contrary, unsupervised CNN-based medical image registration (Li and Fan, 2018; de Vos et al., 2017, 2019; Balakrishnan et al., 2018; Dalca et al., 2018; Stergios et al., 2018; Balakrishnan et al., 2019) does not require ground-truth deformation fields. Instead, these methods (and the original CNN-based unsupervised optical flow estimation method (Ren et al., 2017) ) solve the registration process by minimizing a loss function based on the (dis)similarity M between the deformed source image and the target. They use a differentiable warping module similar that used in spatial transformers (Jaderberg and others, 2015)
, to warp the source image during the forward-pass, and allow the gradients flow back during backpropagation. In such way, the model is trained to produce deformation fields that minimize the similarity-based loss function. At test time, a single forward pass will return the deformation field. In this work, we will follow this strategy to construct a baseline architecture (referred RegNet throughout this text) that will serve as baseline when evaluating the impact of the proposed anatomically constrained registration method.
Various approaches were envisioned in the literature to improve the accuracy and realism of the registration methods by incorporating prior information (about image modalities, anatomy and structure) into the registration process. Two of the most common strategies are knowledge-based transformations, where the information is encoded within the deformation model (Wouters et al., 2006; Glocker et al., 2009; Del Palomar et al., 2008) and segmentation-aware strategies, which directly incorporate segmentation priors to the registration process. The main disadvantage of knowleadge-based transformations is that they are highly domain specific, especially in methods like Del Palomar et al. (2008) , which employ biomechanical models of specific organs to predict real deformations. In this work, we focus on the second alternative. Several non-deep learning based approaches (Shakeri et al., 2016; Ferrante et al., 2017, 2018a) were proposed to take advantage of such segmentations in the context of discrete graph-based image registration (Paragios and others, 2016) . In Shakeri et al. (2016) , probabilistic priors are incorporated to the registration process through a new term in the energy function of the proposed discrete formulation. In Ferrante et al. (2017, 2018a) segmentation masks are used to perform weak supervision when learning to aggregate standard similarity measures for image registration. However, these methods are orders of magnitude much slower than deep learning based image registration models. The first multi-modal CNN-based image registration method proposed in Hu et al. (2018) , incorporates segmentation masks into the loss function of a weakly supervised approach to guide the learning process. They use a pixel-level similarity measure defined on the segmentation masks, that makes it possible to register images independently of their modality. A similar pixel-level measure based on the Dice coefficient was incorporated in the VoxelMorph (Balakrishnan et al., 2019) and U-ResNet (Estienne et al., 2019) frameworks, and used in tandem with a standard intensity based loss. Still, local pixel-level loss functions do not consider the global context and might produce similar values for anatomically plausible and non-plausible segmentations. In this work, we build on top of these ideas by regularizing the learning process using a global and non-linear representation of the underlying anatomy. We show that this global term is complementary to existent pixel-level loss functions. Moreover, in the context of X-ray chest image registration, we improve the performance of existent registration methods by a significant margin while producing more realistic images after deformation.
3 Learning deformable image registration with anatomical constraints
Figure 1: Architecture of the proposed AC-RegNet image registration model. We combine a basic CNN architecture for image registration (upper box) with local (Lce) and a global (Lae) loss functions based on anatomical segmentations SI, SJ (lower box). We train the network VectorCNN to produce deformation fields T which minimize the differences between source (I) and target (J) images while, at the same time, ensuring anatomical correspondence after registration.
In this section, we provide a brief description of the existing basic CNN architecture used to perform unsupervised image registration, which constitutes the building blocks for the proposed model.
We then discuss how can we learn compact and non-linear representations of the image anatomy using denoising autoencoders (DAE)
(Vincent et al., 2010) , and how these representations can be introduced in the loss function to act as an anatomical regularizers, encouraging the learnt model to produce anatomically plausible images after deformation (see Figure 1 for an overview of the proposed novel architecture).
3.1 Basic architecture
The basic CNN architecture for image registration is composed of two main modules. The first one (referred as VectorCNN in Figure 1) follows an encoder-decoder structure similar to that of U-Net (Ronneberger et al., 2015) . Given a pair of source image I and target image J as input, VectorCNN predicts a deformation field T=VectorCNN(I,J;Θ) where T:Rn→Rn (n beign the image dimensionality) and Θ corresponds to the network parameters learnt during training. Images I and J
are concatenated and fed to the network as a single multi-channel image. VectorCNN processes the two images through a series of convolutional and pooling layers, and outputs a 2-channel filter map representing the 2D deformation field (3-channels in case we are dealing with 3D images). The second component is a differentiable warping module similar to that used in spatial transformer networks
(Jaderberg and others, 2015) , that uses T to deform the source image I, producing a warped image I∘T.
At the beginning of the training process, VectorCNN will produce random deformation fields T. During training, the parameters Θ are adjusted so that the warped source image I∘T minimizes the (dis)similarity criterion M with the target image J, in the same spirit that classic registration methods. In this work, we use the negative normalized cross correlation (NCC) to quantify image alignment. NCC has been previously used in the context of CNN-based registration (Balakrishnan et al., 2018) and is a common choice when dealing with monomodal registration. We also consider a simple regularization term R(T) imposing smoothness to the deformation field by computing the total variation of the field (Li and Fan, 2018; Ferrante et al., 2018b) . The basic loss function is therefore defined as
L(I,J,T)=M(I∘T,J)+λrR(T), (2)
where λr is a weighting factor for the total variation-based regularization term.
3.2 Segmentation-aware local loss functions
In order to augment the anatomical context provided to the network, we consider a simple initial strategy to include anatomical segmentations into the loss function by combining the aforementioned intensity-based loss M(I∘T,J), with a segmentation aware loss Lce(SI∘T,SJ). This new loss quantifies the alignment between a target anatomical segmentation mask SJ and a warped version of a source segmentation mask SI. The size of each segmentation mask is the same as that of the corresponding image, and the mask is formed by the elements (or pixels) sk∈C, where C is the set of classes. Lce(SI∘T,SJ)
is implemented as the classical categorical cross-entropy defined at the pixel level on the one-hot encoded versions of
SI and SJ. The segmentation-aware local loss function is thus defined as
L(I,J,SI,SJ,T)=M(I∘T,J)+λrR(T)+λceLce(SI∘T,SJ), (3)
where λce is a weighting factor for the additional term Lce. Note that segmentation masks SI,SJ are only required during training time to compute the loss function. At test time, a single pair of images will be fed into the network to produce a deformation field and no segmentation masks are required.
3.3 Auto-encoding global anatomical priors
The local loss function Lce(SI∘T,SJ) defined in the previous section looks at pixel level predictions; therefore, it does not guarantee a good matching at the global scale between the deformed source and target anatomical masks. The segmentation masks used in this work represent anatomical structures like lungs and heart. This is different from, for example, segmentation masks corresponding to pathological structures or lesions (like brain tumors or skin lesions), which are highly irregular both in terms of shape and topology. Even if anatomical structures present high variability among different patients, human organs maintain a high degree of regularity that we exploit to constraint the registration process. We are interested in designing a loss function to analyze anatomical masks at a global scale, taking into account the anatomical plausibility of the deformed source mask when comparing it with the target mask. Since Lce(SI∘T,SJ)
operates at the pixel level, the back-propagated gradients are parametrized only by pixel-wise individual probability terms and thus provide little global context
(Oktay et al., 2018) .
We learn a lower-dimensional representation of the anatomical segmentations using denoising autoencoders (DAE) (Vincent et al., 2010) . Autoencoders are neural networks designed to learn a mapping from the input space X to a novel, lower-dimensional representation h, that retains significant information about the input. These neural networks usually follow an encoder-decoder architecture, where the encoding h=enc(X) is extracted from an intermediate fully connected layer. This encoding contains significant information to decode the original input through a decoding phase X≃dec(enc(X)).
The model is trained to minimize the reconstruction error of the input masks, what results in maximizing a lower bound on the mutual information between the input X and learnt representation h (Vincent et al., 2010) . In other words, the network is forced to store significant information (useful to reconstruct the original anatomical masks) into the learnt representation. A DAE considers noisy versions of the segmentation masks as input, and is trained to reconstruct clean versions of the corrupted input. This denoising effect, together with the bottleneck imposed by the encoder-decoder architecture, leads the model towards learning a manifold that captures the main variations in the data and maps similar segmentation masks into regions which are close in the manifold.
We train the DAE so that it minimizes the categorical cross-entropy defined over the one-hot encodings of our multi-organ anatomical masks. The noisy input segmentation masks for the DAE were constructed taking the clean segmentation masks and swapping the border pixels of the anatomical structures with the label of its left neighbor with a probability of 0.1. Therefore, the learnt representation concentrates significant information about global anatomical properties of the organs, such as shape and topology, which can be used to introduce global anatomical priors in the learned registration model. In Section 5.1 we perform an experiment to better reflect the advantages of such representation when used as loss function, and discuss why it is beneficial to combine it with standard pixel-level losses.
3.4 AC-RegNet: Learning deformable image registration with anatomical constraints
The novel anatomically constrained registration network (AC-RegNet) architecture is depicted in Figure 1. We combine the basic CNN architecture for image registration described in Section 3.1 with the local segmentation-aware loss function Lce (Section 3.2) and a new loss term based on the learnt anatomical representations (Section 3.3). This term will encourage global agreement between deformed source and target segmentation masks, ultimately resulting in more realistic and anatomically plausible images after warping. The new term Lae is defined as the squared Euclidean distance between the codes h generated from the deformed source SI∘T and the corresponding target segmentation mask SJ as:
Lae(SI∘T,SJ)=∥enc(SI∘T)−enc(SJ)∥22. (4)
Note that both, the Euclidean norm and enc(X) are differentiable operations and therefore Lae is a differentiable loss. The final loss function for our AC-RegNet model considering both, local and global constraints, is given by:
L(I,J,SI,SJ,T) = M(I∘T,J)+λrR(T)+λceLce(SI∘T,SJ)+
λaeLae(SI∘T,SJ).
The influence of the new term Lae in the loss function is controlled with a weighting factor λae. The main difference between Lae and the pixel-level Lce is that the first one acts at a global scale, better reflecting agreement in terms of anatomical shape variations. A deeper study about the complementarity of both losses is provided in Section 5.1.
3.4.1 Training the AC-RegNet model
The training is organized in two stages. First, we train the autoencoder to learn a global and lower-dimensional representation of the anatomical structures using the segmentation masks. Second, we train the AC-RegNet model, by learning the parameters Θ of the VectorCNN that will produce the deformation field T, considering the loss function defined in (3.4). In this second stage, the parameters of the encoder model used to produce the codes h=enc(S) are fixed. We highlight the fact that segmentation masks are used during training (of both, the autoencoder and AC-RegNet models) but, at test time, we only require the pair of images to be registered. This is possible since the CNN which produces the deformation field given the pair of images (referred as "Basic Architecture" in Figure 1) does not use the segmentation masks. Instead, segmentation masks representing anatomical structures of interest in the corresponding images are used during training to enforce anatomical correspondences between the target and deformed source through the loss function defined in (3.4). Once the AC-RegNet model is trained, the registration process of a given pair of images is performed by calculating the deformation field with VectorCNN and deforming the source image with the Warper module (see upper block in Figure 1), without the need of segmentations. The anatomical constraints are therefore introduced in the model during training.
4 Data and Experimental Setup
4.1 Image dataset
The proposed registration model is evaluated in the context of inter-subject 2D chest X-ray image registration. Performing such task for different patients is challenging, since the anatomical variability between two different subjects can be really high. In our experiments, we use three image databases: the Japanese Society of Radiological Technology (JSRT) database (Shiraishi et al., 2000) , the Montgomery County X-ray database and the Shenzhen Hospital X-ray database (Candemir et al., 2013; Jaeger et al., 2013) . These last two databases were created by the U.S. National Library of Medicine in collaboration with the Department of Health and Human Services, Montgomery County, Maryland, USA and the Shenzhen No.3 People's Hospital, Guangdong Medical College, Shenzhen, China, respectively.
JSRT is a public database containing 247 PA chest X-ray images with and without lung nodules (154 nodule and 93 non-nodule images) of 2048x2048 pixels and a spacing of 0.175 mm/pixel. The Montgomery set contains 138 PA X-ray images with and without manifestations of tuberculosis (80 normal and 58 pathological images) of 4020x4892 or 4892x4020 pixels and a spacing of 0.0875 mm/pixel. The Shenzhen set contains 662 X-ray images with and without manifestations of tuberculosis (326 normal and 336 pathological images) in different sizes. Spacing is not provided, so we report results in pixel space when computing distance based measures like Hausdorff distance, which measures the maximum distance between segmentation contours111We used the Hausdorff distance implementation available in MedPy package.. JSRT provides manual lung and heart segmentations for each image. Manual lung segmentations are available for Montgomery and Shenzhen sets. These segmentation masks will be used to learn the lower-dimensional representations and introduce anatomical context to the registration problem.
The images and segmentations of the Montgomery and Shenzhen sets were preprocessed in order to obtain square images in the same spatial resolution. In each dataset, an image was taken as a reference image and resized by filling its shortest side with background color to make it square. Then, all the images of each dataset were registered against this image, taken as a reference image, through a similarity transform using SimpleElastix (Marstal et al., 2016) 222The configuration files used to run Elastix can be found online at https://github.com/lucasmansilla/ACRN_Chest_X-ray_IA/tree/master/acregnet/config/JSRT/elastix, finally obtaining images of 4892x4892 pixels in the Montgomery set and 3000x3000 pixels in the Shenzhen set.
4.2 Experimental setting
We randomly divided the images of each dataset in 60% training, 20% validation and 20% test. In the training stage, we sample random pairs of images from the training fold and built mini-batches of size 32. For testing, we sample 2×N random pairs of images from the test fold, where N represents the number of images in that fold of the dataset.
In order to evaluate the performance of image registration algorithms we employ three metrics commonly used in the literature, which quantify the agreement between the warped source segmentation after registration and the target masks: (i) Dice Similarity Coefficient (DSC), which measures the overlapping between the segmentations (Dice, 1945) , (ii) Hausdorff Distance (HD), maximum distance between segmentation contours, and (iii) Average Symmetric Surface Distance (ASSD), computed as the average distance between the segmentation contours. DSC varies between 0 and 1, with 1 indicating a total correspondence between segmentations. HD and ASSD measure the distance between contours in milimeters, and lower values indicate better performance.
4.3 Implementation details
The proposed models were implemented in TensorFlow and trained with Adam optimizer considering learning rate of
10−3 and default TensorFlow values for the remaining optimization meta-parameters333Our code is available at https://github.com/lucasmansilla/ACRN_Chest_X-ray_IA. Requirements and instructions are listed at https://github.com/lucasmansilla/ACRN_Chest_X-ray_IA/tree/master/CLI_application/acregnet. Models were trained until convergence and the weighting factors for the loss functions were chosen trough grid search using the validation fold, resulting in λr=5×10−5, λce=1 and λae=10−1. A detailed description of the CNN architectures is provided in A.
5 Results and Discussion
Figure 2: Comparison between the local loss Lce defined at the pixel level, and the global loss Lae based on the learnt representation, when comparing 20 random reference segmentations from JSRT dataset with its modified versions. The modified masks were obtained by manually setting 120 foreground pixels to background forming anatomically plausible (a) and non-plausible (b,c,d) versions. The figure shows that Lae encodes complementary information with respect to Lce. Note that while the local Lce remains constant, the global Lae is much lower for the anatomically plausible cases. This indicates that the proposed Lae loss can discriminate different degrees of anatomical plausibility in cases where the standard pixel level functions indicate equivalent accuracy.
5.1 Understanding the anatomical constraints
We perform a first experiment to compare the behaviour of the proposed global loss function (Lae) with the standard pixel-level loss (Lce), when comparing anatomically plausible and non-plausible segmentation masks. We take 20 random segmentation masks from our dataset, and generate 4 modified versions of each one by changing a constant number of pixels (120 pixels in our example) from the original segmentation (see images (a), (b), (c) and (d) in Figure 2). While the segmentation mask (a) corresponds to an anatomically plausible version of the original mask (we just erode the mask by changing to background 120 pixels in the lungs and heart border), the other versions correspond to anatomically non-plausible masks (we remove blocks of 120 pixels representing complete parts of the lungs or heart). Remind that, in all these cases, a fixed number of pixels was changed. We then compute both losses Lae, Lce and compare the reference segmentation with its modified versions. As expected, the local Lce remained constant for the 4 cases, regardless of the place where the pixels are modified, since the number of non-agreeing pixels was 120 for all of them. However, when observing the behaviour of the global loss Lae, it returned a much lower value for the anatomically plausible case than for the non-plausible cases. Figure 2 shows the loss value for the 20 modified random masks following the same tendency: while the local Lce remained constant in all cases, the global Lae returned higher values for the non-plausible masks.
Dataset Method DSC HD ASSD
AC-RegNet 0.943 (0.020) 17.973 (7.356) 3.340 (1.210)
AE-RegNet 0.934 (0.021) 19.464 (8.277) 3.846 (1.320)
JSRT CE-RegNet 0.925 (0.025) 21.973 (8.966) 4.466 (1.553)
RegNet 0.809 (0.085) 42.177 (19.751) 11.229 (5.035)
SimpleElastix 0.846 (0.087) 35.713 (18.180) 9.028 (5.050)
Montgomery CE-RegNet 0.929 (0.027) 33.425 (22.813) 4.349 (1.945)
RegNet 0.869 (0.052) 45.152 (35.702) 8.078 (5.002)
AC-RegNet 0.931 (0.027) 277.386 (182.207) 31.738 (15.891)
AE-RegNet 0.924 (0.032) 285.549 (179.823) 34.452 (18.259)
Shenzhen CE-RegNet 0.908 (0.039) 325.958 (201.213) 42.845 (23.560)
RegNet 0.830 (0.073) 410.012 (225.783) 73.758 (35.849)
SimpleElastix 0.883 (0.058) 353.562 (217.423) 51.978 (30.299)
Mean and standard deviation of Dice Similarity Coefficient (DSC), Hausdorff Distance (HD) and Average Symmetric Surface Distance (ASSD) along all classes (left/right lung and heart) from JSRT, Montgomery and Shenzhen datasets. HD and ASSD for JSRT and Montgomery are expressed in milimeters, while Shenzen is expressed in pixels. Differences among the distributions for all pairs of method are statistically significant according to a paired Wilcoxon test considering Bonferroni correction.
Note that AC-RegNet achieves the best results, presenting the highest DSC and lowest HD and ASSD.
This confirms our intuition about how Lae encodes complementary information with respect to Lce. In the next section, we will see how this sensitivity to anatomical differences at the global scale can be exploited to improve the accuracy of our registration algorithm.
Figure 3: Visualization of the results after registering a pair of images. The blue and red circles highlight two of the areas of the lung anatomy that is better preserved by the AC-RegNet, when compared with the basic RegNet and the other segmentation-aware models. Note, for example, that unlike AC-RegNet, other methods such as SimpleElastix (row 3) and RegNet (row 4), produce images where lungs are cut or stretched down.
5.2 Model comparison
The proposed AC-RegNet model was compared with two initial baselines: SimpleElastix (Marstal et al., 2016) and the baseline RegNet described in Section 3.1, which do not consider segmentation-aware loss functions during training. SimpleElastix is a classic medical image registration toolbox, considered state-of-the-art and listed as one of the most popular software packages implementing iterative image registration during the last 20 years (see Viergever et al. (2016) ). It has been recently used as baseline method not only for chest X-ray image registration (Ferrante et al., 2018b) but also in a variety of applications (see for example de Vos et al. (2019) or a complete list of publications in the Elastix website444List of publications using Elastix and their corresponding parameter files: http://elastix.bigr.nl/wiki/index.php/Parameter_file_database.).
We also include two segmentation-aware models, one considering only the local Lce loss (referred as CE-RegNet for Cross Entropy) and another one considering only the global anatomical loss Lae (referred as AE-RegNet for Auto-Encoder). The proposed model AC-RegNet considers a combination of both losses, as described in (3.4). The quantitative results reported in Table 1 show that our results are comparable to the state-of-the-art (Larrazabal et al., 2019; Candemir and Antani, 2019)
. Statistical significance of the results between each pair of methods was verified by performing a Wilcoxon signed-rank test considering Bonferroni correction for each evaluation metric. In particular, when comparing the two most accurate methods (AC-Regnet and AE-RegNet) we obtained
p<0.005 in JSRT, p<0.05 in Montgomery and p<0.005 in Shenzhen dataset. Table 1 shows that all segmentation-aware strategies (AC-RegNet, AE-RegNet and CE-RegNet) outperform baseline models (SimpleElastix and RegNet) by a significant margin, giving the highest DSC and lowest HD and ASSD values in each dataset. This already indicates that providing an anatomical context to the network helps to improve performance. Moreover, using the combined local and global metrics (AC-RegNet) yields better performance than the individual cases. Figure 3 illustrates the regularization effect produced by the AC-RegNet when compared with the other models. Note that registering images with AC-RegNet ensures a better anatomical correspondence between the organs, preventing unrealistic shapes in the lung areas as it happens with methods such as SimpleElastix and RegNet (where lungs are cut or stretched down). These results confirm our previous study about the complementarity of both loss functions (see Section 5.1), and the importance of considering global shape information on top of pixel-level descriptors to obtain more anatomically plausible results.
5.3 Applications to X-ray image analysis
In this section we aim at highlighting the potential of AC-RegNet in a variety of medical image analysis tasks. We show three different applications of the proposed method in X-ray images: (i) multi-atlas image segmentation, (ii) reverse classification accuracy (RCA) estimation (Valindria et al., 2017) and (iii) representation learning for pathology classification. We use the well known NIH Chest-XRay14 dataset (Wang et al., 2017)
that includes 112,120 chest X-ray images labeled with 14 common thorax diseases according to an automatic natural language processing (NLP) analysis of the radiology reports.
Multi-atlas image segmentation: Anatomical segmentations are useful when performing disease classification and population analysis. The Chest-XRay14 is one of the largest medical datasets publicly available. However, it does not include anatomical segmentations. We used the AC-RegNet model to implement a multi-atlas segmentation model (Iglesias and Sabuncu, 2015) and produce anatomical masks of lung and heart for all the images, which we are making publicly available555The resulting anatomical segmentation masks together with their corresponding RCA coefficient that estimates the quality of the segmentation can be downloaded from: https://github.com/lucasmansilla/NIH_chest_xray14_segmentations. We follow a simple multi-atlas segmentation strategy (Mansilla and Ferrante, 2018) : given a target image, we take the 5 most similar images from the JSRT dataset (those which maximize the normalized cross correlation with that image) and apply AC-RegNet to register all of them to the target image space. We then transfer the JSRT segmentation labels by applying the resulting deformation field and fuse them using a simple majority voting mechanism.
We believe that these segmentations are a valuable by-product contribution of our work, which may be used by the medical imaging community to perform further analysis based on the Chest-XRay14 dataset. We conducted automatic quality control to estimate the accuracy of the segmentation using RCA as described in the following paragraph.
Figure 4: Histogram of estimated Dice coefficients for the resulting Chest-XRay14 segmentations. After visual inspection, we manually set a threshold of 0.92 to perform quality control and decide whether a segmentation meets (in green) or not (in red) the minimum quality standards. We include some visual examples, which represent segmentation masks of various qualities obtained with the propose method, together with their corresponding RCA-based predicted Dice.
Reverse classification accuracy (RCA) estimation: RCA is a framework for predicting the performance of a segmentation method on unseen data, first introduced in Valindria et al. (2017)
. RCA takes the predicted segmentation from a new image to "train" a reverse classifier (reverse in the sense that it is trained using a prediction), which is evaluated on a set of reference images with available ground truth. Such reverse classifier may take different forms, ranging from a random forest classifier to a single-atlas segmentation method based on image registration. The hypothesis is that if the prediction is correct, then the RCA classifier trained with that predicted segmentation will perform well in the reference images (those with ground truth). For a more detailed description of RCA see
Valindria et al. (2017) .
Here we use AC-RegNet to implement RCA based on image registration, estimating the Dice coefficient of the Chest-XRay14 anatomical masks considering JSRT as reference images with ground-truth annotations. In such way, we provide an estimated quality index associated with every segmentation mask, that can be used to define if a given segmentation is to be trusted or not. After visual inspection, we set that threshold in 0.92. Figure 4 shows the histogram of RCA Dice coefficients together with some visual examples of segmentation masks below and above the minimum quality threshold.
Cardiomegaly classification: Cardiomegaly refers to an enlarged heart seen on any imaging test and can be diagnosed based on the cardiothoracic ratio (CR) (Dimopoulos et al., 2013) . Since CR can be computed using the boundaries derived from heart and lung masks (Li et al., 2019) , the anatomical segmentations should provide enough information to distinguish between healthy and pathological cases. We evaluated the discriminative power of the segmentations which meet the minimum quality requirements (RCA Dice > 0.92) in the task of cardiomegaly vs healthy control classification. After quality control, we kept 87,870 from the original 112,120 images, out of which 2019 were labeled with cardiomegaly. We also sampled 2019 healthy patients with RCA Dice > 0.92 to create a balanced dataset of 4,038 images including control and pathological.
We perform 20-fold cross validation on the aforementioned dataset training a support vector machine (SVM)
(Cortes and Vapnik, 1995)
with two alternative inputs based on the segmentation masks. As a first alternative, we applied principal component analysis (PCA) to reduce the dimensionality of a vectorized version of the segmentation masks
666We employ the Scikit-learn ( https://scikit-learn.org/ ) implementation of PCA and SVM in our experiments., keeping the 32 principal components and using them as features to train the SVM. Second, we employ the 32-dimensional representation learnt by the same autoencoder used to impose anatomical constraints to the AC-RegNet model. We trained the SVM using these two alternative representations and obtained accuracies of 0.77 and 0.79 respectively. This suggests that the learnt representation encodes useful information that can be exploited in other medical imaging scenarios.
6 Conclusions
In this paper, we introduced a new method to regularize CNN-based deformable image registration by considering global anatomical priors in the form of segmentation masks. Our method learns a non-linear and compact representation of the anatomy associated with medical images, and uses it to constraint the training process of standard CNN-based image registration architectures. Our method outperforms baseline approaches using 3 different datasets of X-ray images . What is more, we have used the to showcase different application scenarios where the proposed method can be used beyond standard image registration tasks, to perform multi-atlas segmentation, reverse classification accuracy estimation and pathology classification.
We provide a comprehensive evaluation of the AC-RegNet model in a challenging problem like chest X-ray image registration, including quantitative and qualitative results in three different datasets (namely JSRT, Montgomery and Shenzhen). We also showcase three different application scenarios in the context of X-ray image analysis using the NIH Chest X-Ray dataset (composed of more than 100,000 images), where the proposed AC-RegNet was used to perform image segmentation, quality control and pathology detection. In conclusion, we found that the proposed global loss function encodes significant information about the anatomical plausibility of a deformed segmentation mask, which complements existent local losses defined at the pixel level. When used in tandem with intensity-based metrics and local losses defined on the segmentation masks, the global loss introduces additional constraints during training that encourage the registration model to produce more anatomically plausible images after deformation.
The proposed model was applied in the context of 2D image registration, but extending it to 3D images is straightforward. In the future, we plan to validate our model in the context of brain 3D image registration, where anatomical structures can be clearly identified and used to constraint the training process. Moreover, as suggested in Hu et al. (2018) , CNN-based image registration methods considering segmentation masks can help to alleviate the challenging task of multi-modal registration. We plan to explore how AC-RegNet can be used to develop fast, reliable and realistic image registration methods for multi-modal scenarios.
7 Acknowledgments
EF is beneficiary of an AXA Research Fund grant. The authors gratefully acknowledge NVIDIA Corporation with the donation of the GPUs used for this research, and the support of UNL (CAID-PIC-50220140100084LI and 2016-082) and ANPCyT (PICT 2014-2627, 2018-03907, 2018-3384).
Appendix A Detailed architectures
Feature Maps
(Height×Width×Channels)
Input 64×64×4
Conv + BN 16 32×32×16 3×3 2×2 1×1
ReLU 32×32×16
Conv + BN 1 8×8×1 3×3 2×2 1×1
ReLU 8×8×1
ReLU 64
Up + Conv + BN 32 16×16×32 3×3 1×1 1×1
Conv 4 64×64×4 3×3 1×1 1×1
Structure of the Autoencoder. BN: Batch Normalization. ReLU: Rectified Linear Unit. FC: Fully Connected. Up: Upsampling by a factor of 2 with nearest neighbor interpolation.
ELU 64×64×16
Conv + BN (a) 16 64×64×16 3×3 1×1 1×1
Average Pooling 32×32×16 2×2 2×2 1×1
Conv + BN (b) 32 32×32×32 3×3 1×1 1×1
Conv + BN (c) 64 16×16×64 3×3 1×1 1×1
Average Pooling 8×8×64 2×2 2×2 1×1
Conv + BN 128 8×8×128 3×3 1×1 1×1
ELU 8×8×128
Dropout (50%) 8×8×128
Concat with (c) 16×16×64
Dropout (50%) 16×16×64
Concat with (b) 32×32×32
Concat with (a) 64×64×16
Conv + BN 2 64×64×2 3×3 1×1 1×1
Table 3: VectorCNN takes the source and target images as input (64x64), which are concatenated and feed into the network. It predicts a 2D deformation field, which has the same resolution as the input images. References: BN: Batch Normalization. ELU: Exponential Linear Unit. Up: Upsampling by a factor of 2 with nearest neighbor interpolation.
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Students identify a problem in everyday life.
Students use their creativity and skills to innovate a solution through technology, craftsmanship, or a combination of the two.
Students present their prototypes and projects for adjudication on February 16.
Projects may be created by an individual or a group.
Projects will be judged in two separate categories and age groups: Middle School Technology, Middle School Craftsmanship, High School Technology, and High School Craftsmanship. Middle School is grades 6-8 and High School is grades 9-12.
Students and their sponsoring teacher will receive a complimentary ticket to The Hundred Dresses performance held on the day of the challenge. Friends and family members of competing students will have the opportunity to purchase discounted tickets to the same performance.


Over the past few weeks, we have been learning about different classical music composers in our music theory classes. Sometimes we can be so focussed on playing the music that we forget about the forces behind the creation of such wonderful pieces!
This past week we learnt about Sibelius, perhaps the all time greatest Finn in the realm of classical music. Having been born in Finland and spending the first 18 years of my life there, it feels appropriate to share some knowledge of this great composer – in particular since Finland has always been considered to be a relatively small country in the back burner even if many brilliant things came from there such as Nokia and Kimi Raikkonen, not to forget the amazing education system that always tops any leader boards on education outcomes!
Jean Sibelius, born Johan Julius Christian Sibelius (8 December 1865 – 20 September 1957), was a Finnish composer and violinist of the late Romantic and early-modern periods. He is widely recognized as his country's greatest composer and, through his music, is often credited with having helped Finland to develop a national identity during its struggle for independence from Russia.
The core of his oeuvre is his set of seven symphonies which, like his other major works, are regularly performed and recorded in his home country and internationally. His other best-known compositions are Finlandia, the Karelia Suite, Valse triste, the Violin Concerto, the choral symphony Kullervo, and The Swan of Tuonela (from the Lemminkäinen Suite). Other works include pieces inspired by nature, Nordic mythology, and the Finnish national epic, the Kalevala, over a hundred songs for voice and piano, incidental music for numerous plays, the opera Jungfrun i tornet (The Maiden in the Tower), chamber music, piano music, Masonic ritual music, and 21 publications of choral music.
Sibelius composed prolifically until the mid-1920s, but after completing his Seventh Symphony (1924), the incidental music for The Tempest (1926) and the tone poem Tapiola (1926), he stopped producing major works in his last thirty years, a stunning and perplexing decline commonly referred to as "The Silence of Järvenpää", the location of his home. Although he is reputed to have stopped composing, he attempted to continue writing, including abortive efforts on an eighth symphony. In later life, he wrote Masonic music and re-edited some earlier works while retaining an active but not always favourable interest in new developments in music.
The Finnish 100 mark note featured his image until 2002, when the euro was adopted.Since 2011, Finland has celebrated a Flag Day on 8 December, the composer's birthday, also known as the "Day of Finnish Music".In 2015, the 150th anniversary of the composer's birth, a number of special concerts and events were held, especially in the city of Helsinki.
This entry was posted in Educational work, Music work, Writing & blogging and tagged classical music, composer, East London, finland, finnish, hackney, music education, music theory, sibelius, Tiija Rinta on October 1, 2018 by Buna School of Music.