1. The Doctrine of the Scriptures: We believe in the complete inspiration of the accepted canon of Scripture as originally given. We believe that the Bible not only contains the Word of God, but is the Word of God to man. The Scriptures of the Old Testament and the New Testament were given by inspiration (God-breathed) of the Holy Spirit, using the faculties of the various writers but overruling their imperfections, thus giving to us the only inspired, infallible and authoritative revelation – The Word of the living God. The Scriptures therefore are the final authority for all matters of faith and practice (2 Timothy 3:1617; 2 Peter 1:20-21; Psalm 138:2; 1 Chronicles 28:11,19).
2. The Doctrine of God: We believe in the Eternal Godhead, Trinity in Unity (Tri-une) who has revealed Himself as one God existing in three Persons, even the Father, the Son and the Holy Spirit: distinguishable but indivisible; co-eternal, co-equal and co-existent in attributes, nature, power and glory. This Self-revelation of God in His tri-unity is revealed in the Scriptures alone. The one true Self-existent God is Eternal, Omnipotent, Omnipresent, Omniscient and Immutable (Matthew 3:16-17; 28:19; 2 Corinthians 3:14; 1 John 5:7-8; Ephesians 2:18; Isaiah 48:16; John 14:16-17; Genesis 1:1; Romans 1:20; Hebrews 11:6).
3. The Doctrine of Christ: We believe that the Lord Jesus Christ is the Eternal Son of God, who pre-existed with the Father, and by His incarnation and virgin birth He took on Himself the form of man and was revealed as the Godman. We believe that in Him the Divine and Human natures are so united that He is truly and properly God and truly and properly man, thus constituting Him as the only mediator between God and Man, and the Saviour of all men (John 1:1-3, 14-18; Matthew 1:18-23; Luke 1:30-33). This is to say that we believe in His Pre-existence, His Divinity, His Virgin Birth, and His Sinless Humanity. Upon these facts stand His Substitutionary Death, Burial, Resurrection, Ascension and Exaltation and Glorification to the throne of God, from whence He will come again "in like manner" the second time without sin unto salvation (Acts 2:34-37; Psalms 110; Micah 5:2; John 17:5; Isaiah 7:14; 9:6; Philippians 2:5-11; Acts 1:10-11).
4. The Doctrine of the Holy Spirit: We believe that the Holy Spirit in the third Person in the eternal Godhead, is Deity, and co-equal in nature, power, attributes and glory with the Father and the Son, proceeding from the Father through the Son. The Holy Spirit is not merely an influence, but the very Personality of the Godhead and executor of the will and plan of God (Matthew 28:19; 2 Corinthians 13:14; John 14:16-17; 15:26).
5. The Doctrine of the Covenants: We believe that the God of the Bible is a Covenant-making, Covenant-keeping and Covenant revealing God; that man can either accept or reject the Covenants but cannot change them. Man does not originate the Covenants but is simply the recipient of such.
The great Covenants of the Bible are the Edenic, Adamic, Noahic, Abrahamic, Mosaic, Land of Israel Covenant, Davidic and New Covenants. The major dealings of God with man are summed up in the Old Covenant of LAW and the New Covenant of GRACE. All Covenants are founded on and are the expression of the Everlasting Covenant made in the counsels of the Eternal Godhead before sin, and cover God's purpose for man in Creation and Redemption (Genesis chapters 1; 3; 8; 12; 15: Exodus 19-24; Deuteronomy 29-32; 2 Samuel 7; Matthew 26:26-28; Hebrews 8; Jeremiah 31:31-34; Hebrews 13:20).
6. The Doctrine of Creation: We believe that God is the Creator, preserver and Sustainer of Heaven and Earth and everything therein, and that all creation and creatures were created by Him and for His pleasure and all are dependent upon the Creator for their existence. None are independent but all are beneficiaries of Divine providence and care (Genesis 1:1; 2:1-4; John 1:1-3; Colossians 1:16-17; Revelation 4:11; Acts 17:23-31).
7. The Doctrine of Angels: We believe that God created angelic beings, spirit beings, who are ministering spirits doing the will of God, and are servants to the heirs of salvation. Their chief delight is to worship God, not be worshipped, and to do His good and perfect will (Hebrews 1:7; 14: Psalm 91:11; Matthew 25:22,41; 1 Timothy 5:21; Hebrews 12:22-24; Psalm 104:4; Revelation 4-5).
8. The Doctrine of Satan and Demons: We believe in the reality and personality of Satan and fallen angels. The Bible shows that there were fallen angels who sinned along with their leader, Satan. Also that there are angels who did not rebel and these are spoken of as Elect Angels. Satan and his fallen angels, and demonic spirits, are reserved unto judgment of the great day, and will spend eternity in the Lake of Fire and Brimstone, which was prepared for them (Matthew 25; 14; 2 Peter 2:4: Jude 6; 1 Timothy 5:21; Revelation 2:7-9; 20:11-15; 14:9-11).
9. The Doctrine of Man: We believe that man is the highest creation of God, made in the image and likeness of God: that man was created in a state of innocence and dependency upon God: that man is a triune being, consisting of spirit, soul and body: that God, for God, to be filled with, created man God. We also believe that man was created with a free will and is therefore a moral, responsible and accountable creature (Genesis 1:26-29; 2:7; 16-17: 1 Thessalonians 5:23; Psalm 8:5-8; Deuteronomy 30:15-19; Ecclesiastes 7:29; Romans 14:12).
11. The Doctrine of Sin: We believe that sin and "the mystery of iniquity" began in Lucifer, the Archangel and anointed Cherub covering the throne of God, and that sin entered the human race by this fallen angel, who became known as the devil or Satan (Isaiah 14:12-14; Ezek. 28; John 8:44; 1 John 3:8). The devil brought about the Fall of Man under probation, and thus man fell from his state of innocence and "in Adam" all men became sinners, totally depraved in spirit, soul and body, unable to attain Divine righteousness and hence all are under the death penalty which is the "wages of sin" (Genesis 2:1617; Romans 6:23).
12. The Doctrine of Grace: We believe in the grace of God which is manifested towards sinful men. Grace is defined as the undeserved and unearned favour of God bestowed upon sinful men. Man does not deserve it; he cannot earn it; there is nothing in him that merits it. The grace of God includes His love, kindness, goodness and mercy and power by which he builds His church, which comes to men and is through Christ (John 1:14-18; Ephesians 2:7-9; Titus 3:4-7).
13. The Doctrine of the Atonement: We believe that the Atonement is the all-comprehensive plan of God in and for the redemption of fallen man. The old English word "atone" means "to cover, to pardon, to appease, to reconcile" and it is a most comprehensive word in its full ramifications. It was foreshadowed in the Old Testament by the sacrifice of animals, whose blood temporarily "covered" sin. It is fulfilled in the New Testament by Christ's perfect, sinless, human and once-for-all sacrifice, His sinless, incorruptible blood not only "covered" sin. It is fulfilled in the New Testament by Christ's perfect, sinless, human and once-for-all sacrifice, His sinless, incorruptible blood not only "covers" but fully "cleanses" sin (1 John 1:5-7; John 3:16-17; 1:29, 36; Hebrews 9:1-24).
2. Justification – the pronouncing just, a declaration of righteousness and right standing before God through Christ (Romans 5:1; 3:24-26).
3. Regeneration – by which one is born again into the family of God and can call God "Our Father". (John 3:1-5; Matthew 6:9; 1 Peter 1:23).
4. Adoption – whereby one is placed as a son in the family of God (Romans 8:15, 23; Galatians 4:5; Ephesians 1:4-13).
14. The Doctrine of Predestination: We believe that predestination and election are based upon God's foreknowledge. God has not predestined some men to salvation and others to damnation against the free will He gave them. Prophecy concerning persons (such as Judas) was not predestination but foreknowledge. Because God foreknew and foresaw, He (through the prophets) foretold. Prophecy is not predestination but foreknowledge. God is not willing that any should perish but that all should come to repentance (2 Peter 3:9). There is a difference between Provision and Application. Provision for Salvation has been made available for all mankind (John 3:16); but Application of salvation is upon God's terms. God foreknows who will and who will not respond to the conviction and converting power of the Holy Spirit and on that basis He has fixed the destiny of all men, which is either Heaven or Hell. God will never do with His creatures that which is inconsistent with His holy, righteous, and loving character or moral attributes (1 Peter 1:2; Romans 9:11; Ephesians 1:5, 11; Romans 8:28-29).
15. The Doctrine of Salvation: We believe in the salvation of sinners by grace, through repentance and faith in the perfect and sufficient work of Christ on Calvary by which remission of sins is obtained. The word "salvation" includes the meaning of "safety, security, deliverance, and preservation," and "health, wholesome, soundness." It is a comprehensive word and includes in itself God's complete work in the redeemed sinner, reaching in its entirety from regeneration to perfection and glorification.
D. Holy Spirit Baptism (Acts 2:1-4; 1:5-8; 8:14-17; 10:44-46; 19:1-7; Galatians 3:14-15). We believe the Baptism in the Holy Spirit is an experience at or subsequent to salvation, with the Scriptural evidence of speaking in other tongues as the Spirit gives utterance.
G. Eternal Judgment (Revelation 20:11-15; 2 Peter 3:7; Matthew 11:20-25; Acts 17:30-31). The purpose of resurrection is that all men might be judged as to their works done in the flesh, whether they be good or bad, and all will be rewarded accordingly by the supreme judge of all mankind (Romans 14:12; 1 Corinthians 3:9-15; Revelation 20:11-15). This settles the eternal destiny of all men. Believers will be judged at the Judgment seat of Christ and rewarded. The unbelievers will be judged at the Great White Throne and rewarded accordingly.
17. The Doctrine of Divine Healing: We believe in the healing of the body by Divine power, or Divine healing in its various aspects as practiced in the Early Church. God has ordained various means by which he heals; including the laying on of hands, anointing with oil; the prayer of faith by elders of the Church; the gifts of the Spirit; etc (Acts 4:30; Romans 8:11; 1 Corinthians 12:9; James 5:14; Mark 16:15-20; 1 Corinthians 12).
18. The Doctrine of the Kingdom of God: We believe that Christ came to inaugurate the Kingdom of God. This Kingdom is therefore already present but is not yet fully realized. It is seen wherever God's reign is extended and is a present reality in the hearts and lives of believers. The Church is empowered by the Holy Spirit as the agency by which the Kingdom is extended. The Kingdom will be seen in its fullness when Christ returns at the end of the age.
19. The Doctrine of the Church: We believe that the Church is the Body of Christ, and that every genuinely born again believer is called to function as a member in that place set by God in that Body (1 Corinthians 12; 12-31).
The subjugation of Satan and his wicked hosts (Matthew 24:14; Mark 16:15-20; 1 Peter 2:24; Revelation 4:10-11; 5:9-10; 1 Corinthians 11-12-13-14; Ephesians 4:1-16).
20. The Doctrine of Final Things: We believe that the end of the age will close with the literal, actual, visible, bodily and personal second coming of the Lord Jesus Christ in like manner as He went (Acts 1:9-11). At this time those who have died believing in Christ will be resurrected. They will be caught up to meet the Lord in the air, along with those believers who are alive at the time of His return. This is the hope of all believers in all ages. We believe in the millennial reign of Christ.
3. THAT the seven- sealed Book of Revelation will be opened, with its attendant judgments as displayed under the Seven Seals, Seven Trumpets and Seven Vials (Revelation Chapters 4-10; 1516).
5. THAT there will be a personal Antichrist, as well as many antichrists, and his Kingdom will be a Ten-Kingdom Empire, a One-world Government which will be in existence at the second coming of the Lord Jesus Christ (Daniel 2; Daniel 7; Revelations 13-14; 17-18; 2 Thessalonians 2:1-1-12).
9. THAT at the end of the 1000 years, Satan will be loosed out of the bottomless pit, and the second resurrection will take place. The wicked dead, and Satan will attempt a final overthrow of the Camp of God. Fire descends from Heaven. The earth burns. There is the Great White Throne judgment and all whose names are not in the lamb's Book of Life are cast into the Lake of Fire. This is the final and eternal state of the unredeemed, the eternal hell (Revelation 20:715).
10. THAT there will be a New Heavens and a New Earth wherein dwells righteousness. The New Jerusalem will be the capital City of the universe from whence the redeemed of all ages will go in the worship and service of God and the Lamb. Thus there will be eternal punishment for unbelievers and eternal life for believers (John 5:24; 3:16; Mark 9:43-58; Revelation Chapters 2122).


text:
\section{Introduction}\label{sec:introduction}}

\IEEEPARstart{S}{troke} is the most common cerebrovascular disease and is one of the most common causes of death and disability worldwide\cite{1,2}. It is a group of diseases caused by a sudden cerebrovascular rupture or cerebrovascular infraction. The typical symptom of this disease is a focal neurological deficit, such as sudden seizures, language disorders, hemianopia, loss of feeling, etc.\cite{3}. These symptoms may develop into chronic diseases (such as dementia, hemiplegia, etc.), which can seriously affect the life quality of patients; these diseases consume a large part of social health care costs \cite{4}. At the subacute/chronic stages, effective rehabilitation can promote a long-term functional recovery. However, there have been few advances in large-scale neuroimaging-based stroke predictions at the subacute and chronic stages. The most common research scan is a high-resolution T1-weighted structural MRI. Researches using these types of images at the subacture/chronic stages have revealed promising biomarkers. These could potentially provide additional information, beyond behavioral assessments, to predict an individual's likelihood of recovery for specific functions (e.g., motor, speech) and response to treatments \cite{5,6}. Thus far, measures that include the size, location, and overlap of the lesion with existing brain regions or structures, such as the corticospinal tract, have been successfully used as predictors of long-term stroke recovery and rehabilitation \cite{7}. However, a key barrier to correctly analyzing these large-scale stroke neuroimaging datasets to predict outcomes is the accurate segmentation of lesions. As manually-based annotations may no longer be suitable for a wide range of data requirements, there is a need for automatic segmentation tools for their analyses.
\begin{figure}[htbp]
	\centering
	\includegraphics[width=\linewidth,scale=1]{fig1-eps-converted-to}
	\caption{The MRI T1 sequence stroke image from the ATLAS dataset. The first column is the raw data, the second column is the gold standard from the hand-marked lesions by the doctor, and the third column is the combination of the first two columns. Strokes occur in different locations, with large differences in shape and unclear boundaries.} \label{fig1}
\end{figure}

Strokes occur in different locations, with large differences in shape and unclear boundaries as shown in Fig. 1. A public dataset, Anatomical Tracings of Lesions-After-Stroke (ATLAS), is  utilized  to illustrate this variability\cite{7}. Firstly, the segmentation performance is reduced by motion artifacts in the MRI images. Secondly, the position and shape of the lesions are significantly different owing to the existence of multiple subtypes of strokes. The lesion volume can vary from hundreds to tens of thousands of cubic millimeters depending on the severity of the disease, and the lesion area can occur in the cerebrum, cerebellum, and other areas of the brain. Finally, the boundaries of some lesions are not clear, and different clinicians may inconsistently label different lesion areas. Therefore, the accurate automated segmentation is a challenging problem.

To tackle these difficulties, researchers have made many efforts, including intensity threshold processing, region growth, and deformable models. However, these methods rely on the hand-crafted feature extraction by experts; they have a limited feature representation and low generalization performance. In recent years, with the rapid development of deep learning, convolutional neural networks (CNN) have proven to have great potential in the field of medical image analysis \cite{7,8,9,10,11,12,13,14,15,16,17}. The study of CNN is mainly based on two-dimensional (2D) and three-dimensional (3D) approaches: (1) In the 2D CNN approaches, the MRI volume data are converted into several planar slices and independently predict the lesion area of each slice. These ignore the spatial characteristics of the MRI data such that the predictions are discontinuous. (2) In the 3D CNN, approaches, spatial information is extracted for inference. However, due to their computational and storage requirements, the 3D CNN have been largely avoided.

In order to solve the problem of accurately automating the image segmentation, we propose a novel network called the Dimension-fusion-UNet (D-UNet). In this new model, the 3D spatial information in the MRI data is effectively utilized under the 2D framework of the subject and has low computing resource requirements. Our D-UNet has the following two technical achievements:

\textbf{Dimension fusion network:} First, in order to extract the information of consecutive slices from MRI data, we designed a novel downsampling block based on a UNet improvement. This improvement performs 3D and 2D feature extraction on a small number of consecutive slices in the early stage of the network. Then, in a novel way, their respective feature maps are fused to achieve a small number of parameters in the 2D network. Through the extraction of 3D features in the MRI data, D-UNet can achieve better performance than a pure 2D network.

\textbf{Enhanced Mixing Loss:} Second, in order to improve the convergence speed of the network, we propose a new loss function, called the Enhanced Mixing Loss, which not only enhances the gradient propagation of the traditional Dice Loss, but also combines the advantages of the Dice loss and Focal loss functions. This new method converges faster than using the two traditional loss functions, and exhibits a smoother convergence curve. In summary, this work has the following contributions:
1. We propose the D-UNet network to effectively segment the lesion area in the MRI data. The structure is based on the 2D UNet improvement. A part of the 3D convolution is added to the downsampling module to extract the spatial information in the MRI volume data; the extracted features are fused with the 2D structures in a new method.
2. We propose a novel loss function, which is expected to make the network converge in a faster and smoother fashion. It would not only enhance the gradient propagation in the traditional Dice loss, but also combine the merits of Dice loss and Focal loss functions.
3. The proposed method is tested on the ATLAS dataset and compared to three state-of-the-art, demonstrating the superior performance of the method.

{\section{RELATED WORKS}

We summarize some of the work related to stroke segmentation, including hand-crafted feature based methods and deep learning based methods. Among them, the deep learning methods include 2D-based CNN, 3D-based CNN, and the traditional segmentation loss function.

Hand-crafted feature based methods: Researchers have been working on the automatic segmentation and prediction of brain disease areas and have achieved good results\cite{18}. Kemmling et al. \cite{19} use a multivariate computed tomography perfusion (CTP)-based model to calculate the probability of voxelwise infarcts. Kuo et al.\cite{20} propose to use the SVM classifier to learn texture feature vectors for the segmentation of liver tumors. Chyzhyk et al. \cite{21}propose to construct an image data classifier from multimodal MRI data for voxel-based lesion segmentations. Sivakumar et al. \cite{22} use an adaptive neuro fuzzy inference system (ANFIS) classifier to detect and segment brain stroke areas automatically while using the heuristic histogram equalization technique (HHET) to enhance the internal regions of the brain image. These proposals in the literature are machine learning models based on multiple linear regressions, relying on the precise design of features by feature engineers. They achieve good performance on small sized data sets, but have limited generalization in larger data sets.

Deep learning based methods: Deep learning has emerged in recent years, which address a key limitation in traditional machine learning methods, which require engineers to artificially design features. Chen et al. \cite{23} propose a 2D network framework consisting of an ensemble of a DeconvNets (EDD)-Net and a multi-scale convolutional label evaluation net (MUSCLE Net); this ensemble achieves the best performance on a large clinical dataset. Cui et al.  \cite{24} propose a network of cascaded structures for processing nasopharyngeal carcinoma cases in MRI images. The authors firstly segment the tumors and then classify the segmentation results to obtain four subregions of nasopharyngeal carcinoma. These deep learning methods convert the MRI data to 2D slices and apply 2D segmentation CNN for each slice. The 3D results are generated by connecting the 2D segmentation results. However, due to the limitations of the slices' 2D characteristics, the important 3D context information in the volume data is neglected, thus the prediction may lose continuity.

3D CNN has proven to have great potential in the analysis of 3D MRI data. Kamnitsas et al. \cite{25} propose a two-path 3D CNN structure and uses a 3D fully connected conditional random field for post processing, which ranks first in the challenge of chronic stroke lesion segmentation (ISLES 2015). Zhang et al. \cite{10} propose 3D FC-DenseNet, which can make the network deeper by using the improved dense net tight connection structure to enhance the back propagation of image information and gradients. Feng et al. \cite{26} extract features from both the temporal and the spatial dimensions by using 3D convolution operations, which capture the dynamic information in multiple adjacent frames. However, they usually require more parameters and might sometimes over-fit on small training data sets \cite{27,28}. In addition, 2D-based and 3D-based cascade methods have emerged. For example, Li et al. \cite{29} propose a hybrid densely connected UNet (H-DenseUNet), which first performs a 2D-based dense-UNet segmentation, and then uses a 3D-based CNN to correct the spatial continuity of the liver and the tumor.

The binary cross-entropy loss function \cite{30} is commonly used in deep learning based segmentation tasks. This function calculates the gradient by characterizing the difference in the probability distribution of each pixel in the predicted sample and the real sample. Tsung-Yi Lin et al.\cite{31} add a modulating factor to deal with the serious imbalance between the number of foreground and background pixels. Another common loss function is Dice's coefficient loss \cite{32}. This function directly calculates the gradient by the dice overlap coefficient of the predicted sample and the real label; it can also alleviate to some extent the segmentation problem resulting from the pixel imbalance between the foreground and the background.

{\section{Methods}
In this section, we introduce our approach including the proposed D-UNet framework, enhanced mixing loss, and the implementation details. In Section 3.1, we illustrate the proposed D-UNet framework, in Section 3.2, we introduce the enhanced mixing loss algorithm, and finally in Section 3.3, we present some implementation details.

\begin{figure*}[htbp]
	\centering
	\includegraphics[width=\linewidth,scale=1]{fig2.pdf}
	\caption{The entire D-UNet architecture is shown in (a). This network improves 2D UNet, which combines 3D convolution in the downsampling phase and uses a dimension transform block to combine them. (b) Introduces the details of the dimension transform block, which has two branches for its input from 2D and 3D networks. First, the feature channel of the 3D network output (blue arrow) is compressed to 1 by using a 1x1x1 convolution. The compressed result is then squeezed in a spatial dimension and passed to a 2D 3x3 convolution. This makes the output consistent with the 2D network (gray arrow). Finally, each of the channels is weighted by the SE-block and then added together.} \label{fig2}
\end{figure*}

\subsection{D-UNet for extracting three-dimensional information}
The basic structure of the network consists of an improved UNet \cite{33}. This symmetrical encoder-decoder structure combines high-level semantics with low-level fine-grained surface information; it has achieved good effects on medical images. The encoding phase of the D-UNet consists of two dimensions. As shown in Fig. 2(a), both the 2D and 3D convolutions perform the downsampling operation in their respective dimensions; the results are combined through the dimension transform block which denote as a red cube. This fusion enables subsequent 2D networks to be integrated into the 3D information, refines the edges of the target area, and facilitates the ability of the network to identify small lesion areas. Meanwhile, since the 3D information is well extracted in the early stage of the network, and the trainable parameters of the network are extremely increased as the network deepens, the dimension transform block is only added in the early coding stage.

Specifically, consider Fig. 2(b), where H$\times$W denotes the feature dimensions of height and width, D represents the depth in the volume feature, and C represents the channel of the feature map. The dimension transform block consists of 3D dimensionality reduction, channel excitation \cite{34}, and dimensional fusion. The squeeze-and-excite (SE) block has been proposed in recent years, where $r$ denotes the reduction ratio, a hyperparameter which allows us to vary the capacity and computational cost of the SE block [34]. This block activates the connection between different channels by weighting the feature channels. We apply this structure in the dimension fusion block in order to enhance the fusion effect of 3D features and 2D features.

In each dimension transform block, we first reduce the dimensions of the 3D branch feature map and then add with the 2D branch after SE weighted respectively. Specifically, let $I_3d$ and $I_2d$ denote the feature maps from 3D and 2D network respectively, which act as the input of dimension transform block, n denotes the batch size, $h\times w\times d$ denotes the maps' height, width, depth and last dimension c denotes the maps' channel. We first convert I$_{3d}$\(\in\)R$^{n\times h\times w\times d\times c}$ to I$^{*}$$_{3d}$\(\in\)R$^{n\times h\times w\times d\times 1}$ by using a 3D $1\times1\times1$ convolution which filter number is set to 1, then we squeeze the dimensionality of  I$^{*}$$_{3d}$ from $n\times h\times w\times d\times 1$ to $n\times h\times w\times d$. In order to keep the channel number consistent with the 2D branch for later integration, we also convert I$^{*}$$_{3d}$\(\in\)R$^{n\times h\times w\times d}$ to I$^{*}$$_{3d}$\(\in\)R$^{n\times h\times w\times c}$ by using a 2D 3$\times$3 convolution that filter number is set to c. Let I$^{'}$$_{3d}$ denote the I$_{3d}$ after dimensionality reduction:
\begin{equation}
I^{'}_{3d}=f_{r}(I_{3d}),  I^{'}_{3d}\in R^{n\times h\times w\times c}
\end{equation}
where f$_{r}$ indicates the dimensionality reduction operation, thus we convert the size of 3D feature map from I$_{3d}$\(\in\)R$^{n\times h\times w\times d\times c}$ to $I^{'}_{3d}\in R^{n\times h\times w\times  c}$. In order to enhance the feature expression ability of the two dimensions before fusion, we use an SE block to weight the 3D and 2D feature map channels, and add their channel weighted outputs:

\begin{equation}
T=f_{SE}(I^{'}_{3d})+f_{SE}(I_{2d}),        T \in R^{n\times h\times w\times c}
\end{equation}

The 3D and 2D features are fused in this step, where f$_{SE}$ denotes the SE weighted block proposed in \cite{34}. $T$ denotes the feature map fusion which results in the dimension fusion block. More detailed parameter settings for the entire network are shown in Table 1.
\begin{table*}[tp]
	\fontsize{6}{6}\selectfont
	\centering
	\caption{Architecture of the proposed D-UNet.
		'Feature size' denotes the size of the manipulated feature map while the last dimension indicates the channel number. Up-sampling – [*] indicates that the corresponding layer number is concatenated before up sampling and $N^{*}$ indicates the number of features of the corresponding layer number, for example, Up-sampling block 1 being connected to Convolution block 4, $N$ is set to 256, up sampling block 2 being connected to dimension fusion block 3, $N$ is set to 128, and so on.}
	\label{tab1}
	\begin{tabular}{ccccc}
		\toprule
		& \bf Feature size & \bf Two-dimensional operation&\bf Feature size&\bf Three-dimensional operation\cr
		\midrule
		\bf Input					& 192$\times$192$\times$4& -& 192$\times$192$\times$4$\times$1& -\cr
		\midrule
		\bf Convolution block 1		& 192$\times$192$\times$32& 2$\times$(3$\times$3 Conv+ Bn)& 192$\times$192$\times$4$\times$3&  2$\times$(3$\times$3$\times$3 Conv+ Bn)\cr
		\midrule
		\bf Pooling					& 96$\times$96$\times$32& 2$\times$2  max pooling& 96$\times$96$\times$2$\times$32& 2$\times$2$\times$2  max pooling\cr
		\midrule
		\bf Convolution block 2		& 96$\times$96$\times$64& 2$\times$(3$\times$3 Conv+ Bn)& 96$\times$96$\times$2$\times$64& 2$\times$(3$\times$3$\times$3 Conv+ Bn)\cr
		\midrule
		\bf Dimension fusion block 2& 96$\times$96$\times$64& -& -& -\cr
		\midrule
		\bf Pooling					& 48$\times$48$\times$64& 2$\times$2  max pooling& 48$\times$48$\times$1$\times$64& 2$\times$2$\times$2 max pooling\cr
		\midrule
		\bf Convolution block 3		& 48$\times$48$\times$128& 2$\times$(3$\times$3 Conv+ Bn)& 48$\times$48$\times$1$\times$128& 2$\times$(3$\times$3$\times$3 Conv+ Bn)\cr
		\midrule
		\bf Dimension fusion block 3& 48$\times$48$\times$128& -& -& -\cr
		\midrule
		\bf Pooling					& 24$\times$24$\times$128& 2$\times$2  max pooling& -& -\cr
		\midrule
		\bf Convolution block 4		& 24$\times$24$\times$256& 2$\times$(3$\times$3 Conv+ Bn)& -& -\cr
		\midrule
		\bf Dropout					& 24$\times$24$\times$256& -& -& -\cr
		\midrule
		\bf Pooling					& 12$\times$12$\times$256& 2$\times$2  max pooling& -& -\cr
		\midrule
		\bf Convolution block 5		& 12$\times$12$\times$512& 2$\times$(3$\times$3 Conv+ Bn)& -& -\cr
		\midrule
		\bf Dropout					& 12$\times$12$\times$512& -& -& -\cr
		\midrule
		\bf Up-sampling block 1-4	& 192$\times$192$\times$32& 2$\times$2 Up-sampling–[*] &-&- \\  & & \ \ 2$\times$(3$\times$3 Conv+ Bn)\cr
		\midrule
		\bf Convolution				& 192$\times$192$\times$1& 1$\times$1 Conv& -& -\cr

		\bottomrule
	\end{tabular}
\end{table*}

\subsection{Enhanced Mixing Loss Function}
In 3D medical data, especially MRI stroke images as shown in Fig. 1, the volume occupied by the stroke is often very small throughout the scan interval. An extremely large number of background regions may dominate the loss function during training, which leads to the learning process easily falling into a local optimal solution. Therefore, we propose a new loss function, which refers to the method addressing the foreground-background voxel imbalance in \cite{31}, and combines two traditional loss functions in a concise manner\cite{32}.

\subsubsection{Focal Loss}
Focal loss (FL) is an improvement of the binary cross entropy loss (BCE), by adding a modulating factor. This reduces the loss contribution from easy samples and extends the range in low loss. We introduce the formula of focal loss from the binary cross entropy (BCE):

\begin{eqnarray}
FL(p,g) =
\begin{cases}
-\sum_{i=1}^{N_{f}}\alpha(1-p)^{\gamma}\log(p), \ \ \ \ \ \ \ \ \ if \ g = 1 \\
-\sum_{i=1}^{N_{b}}(1-\alpha)p^{\gamma}\log(1 - p), \ \ \   otherwise
\end{cases}
\end{eqnarray}

where $g\in{0,1}$ represents the ground truth based on the pixel level; $p\in[0,1]$ represents the model prediction probability value, in which 0 denotes the background and 1 is the foreground; $N_f$ and $N_b$ represent the numbers of pixels of class 0 and class 1, respectively;  $\alpha\in(0,1]$ and $\gamma\in[0,5]$ are the modulation factors, which can be flexibly adjusted according to the situation.

\subsubsection{Dice Coefficient Loss}
The dice coefficient loss (DL) mitigates the imbalance problem of background and foreground pixels by modifying the segmentation evaluation index DSC between the prediction samples and the ground truth annotation, showing better performance in the segmentation task:

\begin{equation}
DL(p,g) = 1- \frac{2\sum_{i=1}^{N}p_{i}g_{i}+\delta}{\sum_{i=1}^{N}p^{2}_{i}+\sum_{i=1}^{N}g^{2}_{i}+\delta}
\end{equation}
where $\delta\in[0,1]$ is a tunable parameter to prevent a divide-by-zero error and let the negative samples also have a gradient propagation.

\subsubsection{Proposed Enhanced Mixing Loss}
Based on the above two kinds of loss, we propose the enhanced mixing loss (EML) to increase the convergence speed. First, Log value was used in DL and we invert the value for keeping the value positive, thus enhancing the gradient obtained for each iteration. Then, in order to explore whether the two losses have mutually reinforcing relationships, we also add the focal loss. However, since the focal loss is based on the sum of all voxel probabilities, it is numerically much larger than the dice loss ($DL(p,g)\in[0, 1]$), which plays a leading role in gradient propagation. We hope that the newly added FL and log(DL) contribute equally to EML, so a balance factor of $1/N$ is added to FL to obtain a FL based voxel average. The formula for EML is as follows:
\begin{equation}
EML(p,g)=  \frac{1}{N}FL(p,g)-\log(DL(p,g))
\end{equation}

\subsection{Implementation details}
In the data preprocessing, transverse section images have been selected for this experiment. Within each image, a square area is selected with the diagonal coordinates (10, 40) and (190, 220). This selected area eliminates irrelevant information and enlarges the proportion of stroke lesions in the entire image. Next, the cropped images are resized  $192\times192$ using a bilinear interpolation. Finally, each slice of the processed image is integrated, with a spatial arrangement of two upper slices and one lower slice, forming a matrix of size $192\times192\times4$.
In the downsampling phase, modifications to reduce the total number of parameters from UNet are made. Specifically, the number of filters in the first convolution in the 2D-based and 3D-based streams is set to 32. After each pooling layer, the number of convolution filters is doubled, and finally the number of convolutions in the 2D stream is set to 512. The kernal initialization for each convolution is set using the He's method \cite{35}. A batch normalization is conducted after each layer of convolution to improve the stability of the training. The parameter r in the dimension transform block is set to 16. $\alpha,\gamma,\delta$ in the loss function is set to 1.1, 0.48, 1 respectly to fit our randomly selected dataset. With the SGD optimizer, the learning rate is set to 1e-6. Additional parameter settings are consistent with\cite{36}. We have also employed data augmentation methods to improve the robustness of the model, including setting the input mean to zero translation, scaling, and horizontal flipping. These methods are applied to all of our comparative experiments to ensure fairness. We have trained the models on three 1080TI GPUs. All of the models are trained using the first 150 epochs before validating, to optimize the performance of each architecture without any fine tuning.

\section{EXPERIMENTAL RESULTS AND DISCUSSIONS}
We compare our proposed method to the 2D and 3D convolutional UNet. In this section, we also show the superiority of the proposed loss and discuss the results of the proposed dimension-transform block at different stages.
Datasets and quantitative indicators: We have used the Anatomical Tracings of Lesions-After-Stroke (ATLAS) dataset \cite{7} as our training and validation sets. The dataset contains 229 cases of chronic stroke with MRI T1 sequence scans, in which the size of each case is 233$\times$197$\times$189 while the physical size is 0.9$\times$0.9$\times$3.0mm$^3$; the scans delineate different lesion grade staging. We have randomly selected 183 cases (accounting for the overall 0.8 ratio) as the training set, and the remaining cases as validation sets. We report the model's performance in the Dice Similarity Coefficient (DSC), precision, and recall. DSC is an important indicator to assess the overall difference between our estimates and the ground truths. Recall usually reflects the extent of recall in the lesion area, which is an important reference in clinical practice. We perform threshold processing on all of the prediction results. When the probability that the pixel is predicted to be foreground is less than 0.5, we set it to zero, otherwise it is set to one. In addition, precision evaluates the quality of the segmentation, as the proportion of boundary pixels in the automatic segmentation that correspond to boundary pixels in the ground truth of the image. The quantitative indicator formulae are shown below:
\begin{equation}
DSC=  \frac{2TP}{2TP+FP+FN}
\end{equation}

\begin{equation}
Recall=  \frac{TP}{TP+FN}
\end{equation}

\begin{equation}
Precision=  \frac{TP}{TP+FP}
\end{equation}

where, true positive $(TP)$ indicates that the model correctly predicted voxel. False positive $(FP)$ indicates the voxel that the model classify negative as positive. False negative $(FN)$ indicates that the positive voxel is mistakenly classified as negative by the model.

\subsection{Performance comparison with UNet and its 2D and 3D variants}
We compare the proposed method with the baseline architecture, UNet, both qualitatively and quantitatively. We have trained two versions of UNet to better exploit its performance. The original version uses the parameter settings described in \cite{33}. In the transformed version, the number of convolution kernels is reduced and batch normalization is added after each convolution. Section 3.3 provides a more detailed description of the transform. Furthermore, we compare the proposed method with the 3D structure to prove that our method can extract the 3D information of the data with a small amount of structure. We show the results of different structure outputs, and choose case based DSC, recall, and precision as our quantitative indicators. Furthermore, we also conduct a global based DSC assessment as an auxiliary judgment. It is worth noting that the batch size of all networks is set to 36 during training, except for the 3D UNet. Since the 3D Unet has very high memory demands, the batch size is set to six.

In the quantitative comparison, as shown in Table 2, the UNet (original), UNet (transform), and the proposed method are evaluated. The 2D 'transform' is close to the 'original' in both indicators. However, with respect to the kernel number, the transformation scheme is only half of the original network. It shows that a large number of convolution kernels are unnecessary for this small data set. We have also reduced the number of 3D Unet convolution kernels to match the number of 2D Unet convolution kernels. In addition, all the transform versions and our proposed method adds batch normalization after each convolution layer (except for the last layer of convolution) to ensure convergence stability. The 2D structure achieves a better index than the 3D structure, we consider the following factors: first, due to its huge network structure, 3D network requires a huge amount of time to train, making it difficult to adjust the hyperparameters to optimize performance. Then, Overfitting can easily occur in large network structures, especially if there are fewer training sets (our training set contains only 183 cases). Last but not least, 3D structure requires a large amount of computing resources, which limits the width and depth of the 3D structure by our implementation platform conditions, leading to a decrease in the performance. The results we reported is also consistent with that of \cite{29}, which also shows the phenomenon that the segmentation performance of the 3d structure is lower than the 2d structure. In addition, the time required to train a 3D UNet (140 hours) is about six times that of 2D UNet (23.8 hours). By comparing the segmentation performance and the number of parameters, the metrics for the proposed method are improved by 3.83\% over a single 2D structure, and the number of parameters is only increased by 2\%. These results show that with the addition of 3D structure, the overall performance of the network is improved. It proves that compared with the simple 2D network structure, the proposed dimension transform block can effectively utilize 3D information.

\begin{table}[tp]
	\fontsize{4}{10}\selectfont
	\centering
	\caption{ Comparison results of the proposed method with baseline approaches.}
	\label{tab2}
	\begin{tabular}{c|ccccc}
		\toprule
		\bf Method&\bf \ \ DSC\ \ &\bf DSC(global)&\bf \ \ Recall\ \ &\bf \ \ Precision\ \ \bf&\bf \ \ Total parameters\ \ \cr
		\midrule
		2D UNet(original)&0.4874$\pm$0.2858&0.7117&0.4838$\pm$0.2983&0.5612$\pm$0.3229&31,030,593\cr
		2D UNet(transform)&0.4966$\pm$0.2906&0.7146&0.5038$\pm$0.3044&0.5511$\pm$0.3298&\bf7,771,297\cr
		3D UNet(transform)&0.4710$\pm$0.2877&0.7098&0.4736$\pm$0.3099&0.5531$\pm$0.3247&22,597,826\cr
		Ours			   &\bf0.5349$\pm$0.2763&\bf0.7231&\bf0.5243$\pm$0.2910&\bf0.6331$\pm$0.2958&8,640,163\cr
		\bottomrule
	\end{tabular}
\end{table}

\subsection{Comparison with other state-of-the-art methods}
In this section, we compare the proposed model with other existing well-known frameworks and methods mentioned in \cite{37}. SegNet \cite{38} performs nonlinear upsampling; this is accomplished  using unpooling for the maxpooling index defined in the downsampling phase. Pyramid Scene Parsing Network \cite{39} introduces more context information by using atrous convolution and pyramid pooling. DeepLab v3 plus \cite{40} is the latest version of the Google DeepLab series, which combines the advantages of Atrous Spatial Pyramid Pooling and an encoder-decoder structure. The above methods have achieved good results in the deep learning segmentation task. We compare with these methods to demonstrate the superior performance of the proposed D-UNet. All of the network parameters are configured according to the original articles, except for the minor changes mentioned in Section 4.1; the networks are implemented in our platform. The training set and validation set are strictly consistent (including data preprocessing). During training, the batch size is set to 36. Additional details can be viewed the open source code.

The prediction results obtained with the different frameworks are illustrated in Fig. 3. Seven prediction maps are randomly selected and sorted by the size of the lesion area in ascending order. As shown in the figure, it can be observed that the lesion with a very small foreground area is quite fuzzy, indicating this case is very difficult for the network. In spite of this, our method correctly detects the lesion area. It proves that our approach possesses the ability to identify difficult samples. From the third and fourth rows of Fig. 3, we have found that all models correctly predicted the location of the lesion area in the case of big differences in foreground and background. Furthermore, our method is closer to the ground truth in terms of lesion boundaries. Observing the last two rows, we have found that the proposed method still maintains a good, feature expression ability when the boundary is very blurry. This is because we combine the 3D features and the spatial dimensions to more effectively express the characteristics of the edge blurred lesions.
\begin{figure*}[htbp]
	\centering
	\includegraphics[width=\linewidth,scale=1]{fig3.pdf}
	\caption{Comparisons of our method, Baseline, DenseUnet, DeepLabv3+, PSPNet, and FCN-8s on four different patients.} \label{fig3}
\end{figure*}
In order to show the distribution of the results and prove the stability of the proposed method, we draw a box plot based on the DSC score for each case. These results are shown in Fig. 4. The lower edge of all methods is zero because the ATLAS data set has a very large number of small lesion areas (e.g., the first to third rows of Fig. 3), which easily cause the model to fail to recognize the lesions. The upper edge and median line for the proposed method are the best in comparison with other highly recognized methods, which means it not only achieves the highest segmentation performance on a single case, but also yields better median scores for all cases.

\begin{figure}[htbp]
	\centering
	\includegraphics[width=\linewidth,scale=1]{fig4-eps-converted-to.pdf}
	\caption{Box plots of DSC score results for different methods.} \label{fig4}
\end{figure}

To quantitatively illustrate the superiority of our method, we compare the results of these algorithms and summarize them in Table 3, where DSC, recall, and precision are based on the mean $\pm$ standard deviation of each case, and DSC (global) represents the metric based on the voxel calculation. Hu et al. use the ATLAS dataset, and select some specific cases as training/validation sets to summarize some traditional segmentation methods. We compare several deep learning segmentation frameworks with the methods mentioned in \cite{37}. The top half of the table shows the implementation in\cite{37}, and the bottom half is implemented on our platform. It can be seen from the table that the deep learning based methods achieve better performance than the traditional algorithms. With respect to the DSC scores, the proposed method ranked first with a score of 0.7231. This is 0.30 higher than the Clusterize method in DSC (the lowest), and 0.0383 higher than the UNet method (second best). Our method is superior in terms of the segmentation performance for each case. DSC (global) can reflect a voxel-based overall DSC score more intuitively. With respect to recall and precision, the performance of the traditional algorithm Clusterize is the highest in recall, but its precision score is the lowest, which indicates that this algorithm identifies many non-lesional areas as lesions, thus causing a high recall. The proposed method ranked third in the recall score of 0.5243, and the highest in the precision score of 0.6631, which indicates that the identified regions are basically the correct lesion areas.
\begin{table}[tp]
	\fontsize{5}{10}\selectfont
	\centering
	\caption{The quantitative comparison of different methods.
		Among them, DSC, Recall, and Precision are based on the mean $\pm$ standard deviation calculated in each case, and DSC (global) represents the DSC based on the voxel calculation.
	}
	\label{tab3}
	\begin{tabular}{c|cccc}
		\toprule
		\bf Method&\bf \ \ DSC\ \ &\bf DSC(global)&\bf \ \ Recall\ \ &\bf \ \ Precision\ \ \bf\cr
		\midrule
		Clusterize				&0.23$\pm$0.19		&-		&\bf0.79$\pm$0.23	&0.16$\pm$0.15\cr
		ALI						&0.36$\pm$0.25		&-		&0.55$\pm$0.31		&0.31$\pm$0.25\cr
		Lesion gnb				&0.36$\pm$0.23		&-		&0.69$\pm$0.29		&0.30$\pm$0.20\cr
		LINDA			   		&0.45$\pm$0.31		&-		&0.52$\pm$0.34		&0.50$\pm$0.34\cr
		\midrule
		SegNet					&0.3292$\pm$0.2514		&0.5993		&03318$\pm$0.2654		&0.3846$\pm$0.2883\cr
		PSP						&0.4462$\pm$0.2633		&0.6729		&0.4704$\pm$0.2780		&0.4998$\pm$0.2913\cr
		Deeplab v3 plus			&0.4529$\pm$0.2921		&0.7104		&0.4456$\pm$0.3032		&0.5627$\pm$0.3249\cr
		UNet			   		&0.4966$\pm$0.2906		&0.7146		&0.5038$\pm$0.3044		&0.5511$\pm$0.3298\cr
		Ours			   		&\bf0.5349$\pm$0.2763	&\bf0.7231	&0.5243$\pm$0.2910		&\bf0.6331$\pm$0.2958\cr
		\bottomrule
	\end{tabular}
\end{table}

\subsection{Loss validity}
In order to compare the effectiveness of the proposed loss, we trained on the proposed model and compared several common losses in the segmentation task. As illustrated in Fig. 5, the results show several DSC rising graphs on the training sets as the number of training iterations increases. Comparing the scores of the losses in the training set, in the early stage of training (about 30 epoch) shown on Fig. 5, the dice coefficient loss converges faster than the focal loss, but is subsequently exceeded by the focal loss. In each period, our method converges faster than other methods.
\begin{figure}[htbp]
	\centering
	\includegraphics[width=\linewidth,scale=1]{fig5.pdf}
	\caption{The DSC score curve of the training set during the training process.} \label{fig5}
\end{figure}

We also performed a quantitative analysis of the three losses, as shown in Table 4. It is worth noting that our goal is to make the proposed model converge faster. This statistical score is only used as a secondary reference. Since DL directly uses 1 - DSC as punished, it has an advantage to achieve the highest DSC score. Therefore, despite the proposed EML is slightly lower than DL in DSC (0.0115 lower) and Recall (0.057$\pm$0.002 lower), we consider that it is within an acceptable range. On the other hand, EML achieves the highest precision whereas DL presents the lowest, which indicates that EML can effectively reduce the false positive results (This phenomenon is also consistent with FL). Therefore, EML is competitive in segmentation performance compared to DL and FL.
\begin{table}[tp]
	\fontsize{8}{10}\selectfont
	\centering
	\caption{Quantitative analysis of the three loss functions.}
	\label{tab4}
	\begin{tabular}{c|ccc}
		\toprule
		\bf Method&\bf \ \ DSC(global)\ \ &\bf \ \ Recall\ \ &\bf \ \ Precision\ \ \bf\cr
		\midrule
		FL &0.6805&0.4339$\pm$0.2625&0.6225$\pm$0.3667\cr
		DL &\bf0.7346&\bf0.53$\pm$0.2908&0.6143$\pm$0.3324\cr
		EML&0.7231&0.5243$\pm$0.2910&\bf0.6331$\pm$0.2958\cr
		\bottomrule
	\end{tabular}
\end{table}

\subsection{Comparison of dimension fusion blocks between different layers}
We also compare the results of the dimension transform block used in different ways (Add, SE) and in different downsampling layers. The DSC is used as the main evaluation index of model performance, and the number of their parameters is enumerated. The 3D framework used in the experiment, as shown in Fig. 2, has only two pooling layers for the dimension transform block within three layers. The 'add' in Table 5 represents the use of the final fusion operation of the dimension fusion block, (i.e., the Add block in Fig. 2), and the 'SE' indicates the use of the SE block the figure \cite{34}. The last column of the name in the architecture indicates which layer is used in the conversion structure. For example, 'Add-23' means the corresponding 3D structure fusion before the second and third maxpooling in the 2D structure.

In order to prove the validity of dimensional transformation on 2D networks, we compare the performance of UNet (division) and different structures using the dimension fusion block in different layers. All of the results with using the dimensional fusion are better than UNet without using the dimension fusion block. We suspect this is because the network fuses the 3D features in downsampling. By comparing 'Add-1', 'Add-12', and 'Add-23', we find that the deeper the 3D structure is, the better the fusion performance results. This can be interpreted as a deeper 3D structure provides a better feature extraction ability. However, we found an interesting phenomenon: the DSC with more layers in the case 'Add-123' has decreased. This may be because the gradient of the 3D and 2D structure is tighter with the fusion of more layers; this change in gradient may result in a decrease in the efficiency of the feature extraction. We demonstrate the validity of the proposed fusion structure and demonstrate that our method obtains 3D information in an efficient manner.

To prove that the SE block can achieve better fusion by weighting the output of the two dimensions, we show a comparison of the addition of SE (the last three rows) and direct fusion in Table 5. The structure of the SE block is consistent with the direct fusion for the number of layers, but all of the structures using the SE block are higher than the DSC of the direct fusion structure. It is shown that the nonlinear weighting before feature fusion can enhance the fusion effect of features.

\begin{table}[tp]
	\fontsize{8}{10}\selectfont
	\centering
	\caption{ Comparison results of the proposed method with baseline approaches.}
	\label{tab5}
	\begin{tabular}{c|cc}
		\toprule
		\bf Architecture&\bf \ \ DSC\ \ &\bf \ \ Total parameters\ \ \cr
		\midrule
		2D UNet(transform)	&0.4966$\pm$0.2906&\bf7,771,297\cr
		Add-1				&0.5102$\pm$0.2932&7,802,210\cr
		Add-12				&0.5216$\pm$0.2776&7,970,019\cr
		Add-23 				&0.5248$\pm$0.2770&8,635,043\cr
		Add-123				&0.5110$\pm$0.2762&8,636,260\cr
		SE–Add-12			&0.5235$\pm$0.2851&7,971,299\cr
		SE–Add-23			&\bf0.5349$\pm$0.2763&8,640,163\cr
		SE–Add-123			&0.5186$\pm$0.2865&8,647,012\cr
		\bottomrule
	\end{tabular}
\end{table}

\section{Conclusion}
Automated stroke segmentation plays an important role in clinical diagnosis and prognosis. It is of great value to quickly and accurately identify areas of the lesions and help physicians make surgical plans without high computing resource demands. In this paper, we propose an end-to-end training method for the automatic stroke segmentation, in which 3D context information can be effectively utilized, with low hardware requirements. Meanwhile, we propose a new loss function for faster and smoother convergence. The proposed method has been compared with three state-of-the-art methods; it achieves the best performance on two quality metrics (DSC = 0.5349$\pm$0.2763, Precision = 0.6331$\pm$0.2958).
In future work, we hope to increase the punishment for extremely difficult samples inspired from \cite{41}, which may further enhance the performance of the proposed EML. We plan to validate our method on a larger clinical dataset to verify the generalization of the method in the current 3D structure, further study the possibility of dimension fusion block combinations, and extend our model to different applications, for example, calculating overlap of the lesion with existing brain regions or structures, for used as predictors of long-term stroke recovery and rehabilitation.

\ifCLASSOPTIONcompsoc

  \section*{Acknowledgments}
	This research was partly supported by the National Natural Science Foundation of China (61601450, 61871371, 81830056), Science and Technology Planning Project of Guangdong Province (2017B020227012, 2018B010109009), the Basic Research Program of Shenzhen (JCYJ20180507182400762), Youth Innovation Promotion Association Program of Chinese Academy of Sciences (2019351).
\else

  \section*{Acknowledgment}
\fi

\ifCLASSOPTIONcaptionsoff
  \newpage
\fi

redpajama_set_name: RedPajamaArXiv


text:
How to hide my age on tinder
Craig Zacker
@craigzacker
To be honest, age can be a factor against you when you want to find a partner or flirt on Tinder and therefore there are not a few users who wish they could hide or hide this cruel fact in order to have a better dating with the opposite sex in that first impression worth a lot… Well, at Practical Resources.com we want to share the following article: how to hide my age on tinder which aims to clarify a few things about it so that they know what to expect for even the most coveted ones, we bring you a viable solution to stoically fix this woe when it comes to flirting on Tinder.
* By the way, you might be interested in: How to report a user on Tinder …>>
How to Hide My Age on Tinder – Clarifications
First of all, it should be clarified that -unfortunately- from Tinder it is not possible to hide the age they have entered in their user profile. In other words, Tinder does not have an option so that this data is no longer shown to other users when scrolling through profiles, as well as when a user who has matched you enters your profile.
Now, having clarified the above, there is a solution to this age misfortune that can be very reasonable to implement so that this age factor does not act to the detriment when it comes to dating and flirting on Tinder and which consists of: basically - , in changing the age you have in Tinder, adding for this purpose a few more years or removing some others (without exaggeration) depending on your situation so that it works favorably for you.
Below and for those interested, we will explain how it is possible to change the age in Tinder for those who use the App or the web version, which is a more radical option, as well as the change of age in Tinder for those who have connected the own Facebook which It is much less radical than the previous one.
How to hide my age on Tinder – [App – web]
If you use the Tinder app or access the web version of Tinder, you should know that there is no option to change this age in Tinder. However, as we have already announced, there is a radical solution which is to delete your current Tinder account and after that, create a new Tinder account that is of the age that can be conducive for meeting people and flirting.
Note No. 1: It is important to state here that when you delete your Tinder account, all matches and conversations you have in that Tinder account will be deleted and for these reasons, we recommend that you reach out to people you don't want to lose touch with and ask kindly give them some other means of communication so as not to lose sight of them forever.
Change your age on Tinder – [App – web] | STEP 1: Sign in to Tinder
So if they have decided on this solution and have taken the appropriate measures not to lose any contact they have had on Tinder. Then, they will need to log into their Tinder account.
Change your age on Tinder – [App – web] | STEP 2: Delete account and Tinder
They will press or click profile icon which is located in the upper left (if they use a mobile phone).
They will go to their profile and now press or click icon gear-shaped to go to Settings.
This will open the Settings section and here they will scroll down and click on the option that says: Delete account and then confirm this action.
Change your age on Tinder – [App – web] | STEP 3: Create a new Tinder
With the above, open deleted your Tinder account and now it's time to create a new Tinder account just like you did with the previous account, but this time you must be especially careful to put the age you want to be in order for it to act favorably for you and that's it, radical but effective.
How to hide my age on Tinder with Facebook
If you have connected your Facebook account to have a Tinder profile, you should know that to make an age change on Tinder you will necessarily have to do it on Facebook so that this change is reflected in your Tinder user profile.
Note °2: It should be warned here that changing your age on Facebook is limited to a certain number of times and within a range of prudent times and therefore it is advisable to do it only once or twice so as not to have any restrictions in the future.
Change your age on Tinder with Facebook | STEP #1: Login to Facebook
Therefore, anyone who uses a PC or computer must access their Facebook account and with it they will arrive at their Facebook timeline and here they will have to click on their name to access their Facebook profile.
In the meantime, anyone using a mobile must open the Facebook App and once logged in, must click on the three horizontal bars that appear at the top right.
Change your age on Tinder with Facebook | STEP #2: Go to basic information
Once in your Facebook profile, PC users should click on the option that says: «Information» which is displayed under your Facebook cover photo.
Then, the About section will open and here, you can click on the option on the left which says: «Basic and contact information» or if you prefer, you can move the mouse cursor over your date of birth and with To do so, a link will appear that says: «Change your basic and contact information» which you will have to click on, that simple.
Instead, those using the mobile will scroll down and in doing so will find the option that says: «Information» which he will have to press to open this section and again scroll down and in doing so, they will find an item or section called: «Basic information» and they should click, where it says: «Modification» of this element.
Change your age on Tinder with Facebook | STEP #3: Change your age on Facebook
In the next step, PC users should go to the section called: «Basic information» and here, he will hover the mouse cursor over the date of birth and when he does, a link will appear on the right that says: «Modification» which they have to click to change it.
Consequently, the options will be shown to be able to change the date of your birth on Facebook and therefore here, you can click on the day, month and year so that the respective drop-down menus appear and you can select the date of birth that corresponds to a age they want to be for Tinder and after doing so, they will click on the square that precedes the text: «Please confirm that I am x years old» and finally they will click on the button that says: «Save Changes».
Now, those who use a mobile phone will access the date of birth section and here they will click on the icons (triangles) of the day, month and year so that in this way the respective menus will open and it will be possible to select the date they deem convenient to have on Tinder.
Finally, they will save the changes.
Perfect, they did great. They changed their date of birth on Facebook and the new age on their Tinder will be reflected in about 1 day (be patient). This is a palliative option for: how to hide my age on tinder and thus be able to get away with this crucial factor when meeting people and flirting on tinder, good luck.
Note No. 3: One more fact, the birthdate changer Facebook will be able to hide it as such and therefore your age does not appear in your Facebook account before other contacts or friends you have there, this can be done.
How to find friends on tinder ❯
Why am I not getting people on Tinder?
What are the best Tinder alternatives and best free dating apps?
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Our good friends Phil and Jonathan have spent a year across the pond and we couldn't pass the opportunity to visit them (we've really missed them in DC) and of course take advantage of a free place to stay.
Going into London we naïvely had no worries or concerns. They speak English, we knew things would be expensive and they do tea in the afternoon – what more did we need to Google-search before we went? We also knew our priorities for the visit would be simple: spend time with friends, ride a double-decker bus and drink tea with small sandwiches as a snack. OH and enjoy traditional pub fare. Live like the locals, right?
We were also pretty confident in our word-traveling-chops. Having flown across the ocean before, we knew not to make the same mistakes and arrive either exhausted (as we did in Italy, 2013) or hungover (as we did in Copenhagen, 2014). This plane ride we limited ourselves to exactly one movie and one glass of wine. It was sadly uneventful but we did arrive somewhat ready for a day of touristing.
Thank god, too. The Tube line we needed to take to get to Wimbledon was down when we arrived so we quickly needed to figure out the bus system. The bus we needed was a double decker (!!) – check that one off the list! We were also on a time-crunch as Phil told us we were doing tea on a yacht and needed to meet Jonathan on the boat by a very specific time.
I was NOT going to miss this yacht-tea-event. Unfortunately the bus took for-freaking-ever to get around town. After about 30 minutes of weaving around narrow London streets I had had about enough of my double-decker experience and was ready to move on to the next item on my list. A few Dramamine pills later we were walking the streets of Wimbledon (note: it's a neighborhood AND a tennis facility) on our way to the apartment.
Heading to the yacht, Kyle and I were panicked. We were definitely running about 10 minutes late and the assumption was that Jonathan was about to sail off for tea-time without us.
And you know what they say about assumptions … it turns out the yacht doesn't actually sail.
Also, as you can tell from the photos, tea time in London is properly amazing. A perfect way to start this #ZellerTrip!
Going into the Zeller #EruoTrip2k15 (note: I have never used that hashtag) we were saying this was the last "big" trip we'll take in a while. We're homeowners now, we have a mortgage, a new roof to buy and one day we'll update the kitchen (and by "update" I mean tear it to shreds and put in something completely new). People who literally need a roof over their heads shouldn't be spending their money on overseas travel!
And, we were totally okay with that plan. Seriously, we love being homeowners and happy that this is where our money and time is going… but about halfway through this trip we realized that we're just as much in love with travel. I've been struggling ever since this moment to figure out why and how we could love so much these two very different things – the Zellers can't be homebodies AND world travelers, can they?
The above quote is true, however we literally need a roof over our heads. So, we'll just have to live off of the 1,000 or so photos we took on our latest trip and continue the massive ongoing problem solving necessary for our house.


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Here at Canyon Creek Chiropractic, we provide natural, holistic services, and techniques for the entire family that help ease aches and pains while promoting the patient's overall health and state of well-being. You've likely heard that balance is the key to success, and this is also applicable when it comes to your spine and its vital relationship with the rest of your body. Even one spinal vertebra out of alignment can source acute or chronic back pain and impinge on nerve endings that cause discomfort and loss of mobility in connected areas of the body.
Our chiropractic adjustments and supplemental therapies help address painful injuries at the source, so you get long lasting relief as your body heals naturally—typically without the need for surgery or prescription medications.
Dr. Malka Carlucci has years of experience providing effective holistic pain management with the healing abilities of chiropractic care. We welcome you as new patients by offering a no-obligation complimentary consultation with Dr. Carlucci to discuss your pain issues and health concerns. If it's determined by our chiropractor that our services are a fit for your condition, we'll proceed with an examination to explore which techniques will best address your situation.
Chiropractic Adjustments—Dr. Carlucci utilizes a variety of manual and tool-assisted spinal adjustment techniques designed to painlessly realign your spine to promote pain relief and reduce injury healing times.
Spinal Decompression Therapy—One of our most effective tools for correcting lower lumbar injuries is spinal decompression therapy, and many patients are able to avoid surgery for disc herniations and other injuries with this treatment.
Cold Laser Therapy—These treatments reduce pain and swelling and stimulate tissues beneath the dermis to address the root cause of the injury.
Massage Therapy—We offer therapeutic massages for more than relaxation. Dr. Carlucci works closely with our massage therapist to ensure that your treatments complement the goals of your chiropractic care.
At Canyon Creek Chiropractic, we also offer postural screenings, custom corrective exercise plans, nutritional counseling and lifestyle advice that help balance your overall health and sense of well-being.
If you're ready to discover a whole new way of life made possible by spending just a few minutes a week with a chiropractor, call us today at (801) 943-0932 to schedule a free new patient consultation with Dr. Carlucci. We proudly serve the Cottonwood Heights, Midvale, Sandy, Holladay, Murray, Granite and West Jordan, UT.


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To be able to achieve a high positioning in Google, you need to systematically structure your articles. Your content needs to sensibly present the keywords and phrases you are choosing to optimize your site. Individual webpages will belong in distinct keyword phrase groups. Under the main category you should group various key word phrase categories. Use the principal keyword for your site any time you finally optimize your home page. Structuring your website in this manner lets Google understand that you have effectively organized your site. An even more significant benefit is that every page can rank for its unique individual keyword phrase.
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ZIP code 37762 Population Charts
According to the most recent ZIP code 37762 Tennessee demographics data available from the United States Census Bureau released in the American Community Survey in December of 2020, Figure 1 ZIP code 37762 shows it has a Population 2019 of 2,804 which is the third most populous of all other zip codes in the greater ZIP code 37762 region. The zip code with the highest population in the area is 40769 which shows a population of 18,259 ( substantially bigger). Figure 3 uses the ZIP code 37762 population data for a comparison of the population growth/population change estimates from the years 2010 to 2019 and 37762 Tennessee depicts a decline of 322 (10%).
The total ZIP code 37762 Tennessee greater area population percent change for all areas for the years from 2010 to 2019 is shown in Figure 4 and for 37762 depicts it has a Population Change of -10.3% which is less than most other zip codes in the metro area.
Looking at the ZIP code 37762 population density (measured as people per square mile) and providing comparisons to both the national and state average population density in Figure 5, 37762 Tennessee depicts it has 205 people per square mile which is more than all other zip codes in the greater ZIP code 37762 region. The next lower population density is 37766 approximately half the size with population density of 116. Figure 6 provides ZIP code 37762 demographics for the overall median age for all people in the region and 37762 shows it has a Median Age of 48.3 which is the second most median age of all the zip codes in the greater ZIP code 37762 region. The zip code with the highest overall median age of all people in the area is 37715 which shows an age of 49.2 ( only slightly larger). The ZIP code 37762 Tennessee population data for median age broken out by gender for both men versus the median age of women is shown in Figure 7. 37762 Tennessee shows a median age of men about 18.2% smaller as the median age of women.
The next demographic analysis (Figure 8) looks at large generational ZIP code 37762 population groups (and can be useful internet research for employment related research or identifying areas with retirees). Key findings in this chart includes that 37762 Tennessee has the percentage of people less than 20 years of age the second smallest as measured by people less than 20 years of age of all the other zip codes in the metropolitan area at 8.0% of the total. Second, it has the largest proportion of people between 30 and 39 years old at 15.6% of the total and is ranked #1. Third, it has the largest proportion of people between 60 and 69 years old at 19.1% of the total and is ranked #1.
Figure 9 illustrates the overall diversity breakdown for ZIP code 37762 population and ethnicity groups in the area. (note: Hispanic/Latino is broken out separately in the next diagram and also the group denoted as Native ethnicity includes Native American, and Hawaiian and Other Pacific Islander.) 37762 Tennessee has the largest proportion of Native population at 3.2% of the total and is ranked #1. Figure 10 shows the ZIP code 37762 Tennessee Hispanic (including non Hispanic whites) population household ethnicity in the region and 37762 shows it has a Hispanic or Latino of 0.8% which is the third most Hispanic Population of all other zip codes in the greater ZIP code 37762 region. Figure 11 compares the ratio of the amount of men to the amount of women and depicts total male population only slightly larger as the total female population.
ZIP code 37762 Marital Status and Families Charts
Figure 12 shows the detailed marriage characteristics broken down by %residents who are married, never married, single, divorced, and widowed. ZIP code 37762 has the percentage of total percent married the second smallest when ranked by total percent of people married of all the other zip codes in the metropolitan area at 14% of the total. Second, it has the largest proportion of percent divorced at 13% of the total and is ranked #1. Figure 14 compares the average household size using the average number of people in a family for ZIP code 37762 households. 37762 shows it has a Family Size of 3.0 which is less than most other zip codes in the area. The zip code with the highest average family size in the area is 37729 which depicts an average family size of 3.4 (12.3% larger).
Figure 15 shows the overall ratio of ZIP code 37762 households for families to the total number of ZIP code 37762 households and that 37762 shows it has a Families of 54% which is the smallest in terms of percent of people who are in a family of all the other zip codes in the surrounding region. The zip code with the highest percent of people who are in a family in the area is 37729 which shows a families percent of 80% (47.2% larger).
Looking at ZIP code 37762 households that are headed by a husband and wife as a percent of all families in Figure 16, 37762 depicts it has a Married-couple family of 80% which is the second most of all the zip codes in the local area. The zip code with the highest percent of people in a husband and wife family in the area is 37715 which shows a husband and wife family percent of 87% (8.6% larger). Figure 17 shows ZIP code 37762 demographics for the head of household for each place using a breakdown of married-couple, male-headed alone, and female-headed alone. 37762 Tennessee has the percentage of households the second smallest when sorted by percent of families in husband and wife household of all the other zip codes in the metro area at 18.4% of the total.
ZIP code 37762 Mothers and Children Charts
The next section of charts provide a detailed look at mothers and baby births that occurred over the past 12 months. Figure 18 shows the rate of women aged 15 to 50 years old who have given birth. 37762 depicts it has a Birth Rate of 7.3% which is the third most percent of women who have given birth of all other zip codes in the greater ZIP code 37762 region. The zip code with the highest percent of women who gave birth in the area is 40940 which shows a birth rate of 17.0% (approximately 2.3 times bigger). Figure 19 shows the breakdown of the mother's age for all baby births that occurred in the last 12 months and it has one of the largest proportions of percent of births to mothers aged 20 to 24 at 45% of the total and is ranked #3. Only #2 37766 (50%), and #1 40940 (100%) are larger. Second, it has the largest proportion of percent of births to mothers aged 25 to 29 at 21% of the total and is ranked #1. Third, it has the largest proportion of percent of births to mothers aged 40 to 44 at 34% of the total and is ranked #1.
In Figure 21, the percentage of all births in the last 12 months to mothers that were unmarried is shown (unwed mothers.) 37762 shows it has a Unwed Births of 34% which is the third most percent of unwed women who have given birth of all other zip codes in the greater ZIP code 37762 region. The zip code with the highest percent of unwed women who have given birth in the area is 37819 which depicts an unwed mother birth rate of 100% (approximately 2.9 times bigger).
Unwed mothers who have given birth in the last 12 months are also shown broken down by age group in Figure 24. 37762 Tennessee has the largest proportion of unwed mothers aged 20 to 34 at 100.0% of the total and is ranked #1.
Figure 26 shows unwed mothers in poverty (based on median household income definition) who have given birth in the last 12 months. 37762 Tennessee has the largest proportion of unwed births and living below poverty level at 100.0% of the total and is ranked #1.
For all births to unwed mothers, Figure 27 breaks down the education level of the mother by less than high school through college and post graduate attainment it has the largest proportion of percent of unwed mothers with less than high school education at 100% of the total and is ranked #1.
ZIP code 37762 Marital Status Charts
The next set of demographic data looks at the marital status for ZIP code 37762 households which is useful for internet research. Figure 28 compares the total number of single people in each area. 37762 shows it has a Total Single People of 41% which is in the center range of other zip codes in the metropolitan area. The zip code with the highest percent of people who are single for any reason in the area is 37819 which shows a percent single of 57% (39.0% larger). Comparing percent of people who are single for any reason to the United States average of 50%, ZIP code 37762 is about 21.8% smaller. Also, measured against the state of Tennessee, percent of people who are single for any reason of 49%, ZIP code 37762 is about 18.8% smaller.
Figure 30 compares the single people in each area broken down by never married, divorced, and widowed. 37762 Tennessee has the percentage of percent never married in the mid range of other zip codes in the metropolitan area at 14% of the total. Second, it has the largest proportion of percent divorced at 13% of the total and is ranked #1. Figure 31 shows the demographics for the number of single men adults in each area broken out by never married, divorced and widowed. 37762 Tennessee has one of the largest proportions of men who have never been married at 13% of the total and is ranked #2. The only larger zip code being 37819 with 25%. Figure 32 shows the number of single women adults in each area broken out by never married, divorced and widowed. 37762 Tennessee has the largest proportion of women who are divorced at 22% of the total and is ranked #1.
Figure 33 shows the ZIP code 37762 demographics for the ratio of the number of single men between the age of 18 and 65, in each area, broken down by age group for the ZIP code 37762 metro area. 37762 Tennessee has one of the largest proportions of single men 18 to 24 at 18% of the total and is ranked #3. Only #2 37729 (25%), and #1 37715 (26%) are larger. Second, it has less than most other zip codes in the metro area when sorted by single men 25 to 29 at 3% of the total. Third, it has one of the largest proportions of single men 35 to 39 at 12% of the total and is ranked #2. The only larger zip code being 37729 with 22%. Also, it has the largest proportion of single men 40 to 44 at 14% of the total and is ranked #1. In addition, it has one of the largest proportions of single men 45 to 49 at 21% of the total and is ranked #2. The only larger zip code being 37715 with 25%.
Figure 34 shows the ZIP code 37762 population data for the ratio of the number of single women between the age of 18 and 65, in each area, broken down by age group for the ZIP code 37762 metro area. 37762 Tennessee has one of the largest proportions of single women 25 to 29 at 13% of the total and is ranked #3. Only #2 37819 (13%), and #1 40940 (30%) are larger. Second, it has one of the largest proportions of single women 40 to 44 at 9% of the total and is ranked #2. The only larger zip code being 37729 with 15%. Third, it has one of the largest proportions of single women 50 to 60 at 17% of the total and is ranked #3. Only #2 37766 (18%), and #1 40940 (21%) are larger.
ZIP code 37762 Citizenship Demographics Charts
The next section of demographic information provides detailed data ZIP code 37762 residents including: immigrants, citizenship, immigration, country of origin, non citizens and more. To start, Figure 35 shows a high level view of citizenship. 37762 Tennessee has the largest proportion of citizens born In US at 1% of the total and is ranked #1. The next chart (Figure 36) shows for all citizens what the original place of birth was for people who were born in a foreign country and it has the largest proportion of birthplace in northeast US at 17% of the total and is ranked #1. Second, it has the largest proportion of birthplace in the South at 3% of the total and is ranked #1.
In Figure 37, the demographic percentage of ZIP code 37762 residents who were not born in the United States is shown (i.e. percent foreign born.) 37762 depicts it has a Percent Foreign Born of 1.3% which is the second most percent born outside United States of all the zip codes in the greater ZIP code 37762 region. The zip code with the highest percent of population who was born in another country in the area is 37729 which depicts a percent born outside United States of 2.7% (about twice as large). Comparing percent of population who was born in another country to the United States average of 13.6%, ZIP code 37762 is approximately a tenth the size. Also, compared to the state of Tennessee, percent of population who was born in another country of 5.1%, ZIP code 37762 is approximately a quarter the size.
The next diagram (Figure 41) shows the year of entry that non citizens entered the United States. The year of entry can show the rate of flow at various points of time in the past for when they entered the U.S. it has the largest proportion of non citizens who entered the US after 2010 at 100% of the total and is ranked #1.
For all foreign born people who have gone through naturalization (the process of becoming a legal U.S. resident citizen), Figure 42 shows the year when they became fully naturalized U.S. citizens. it has the largest proportion of people naturalized 2010 to 2014 at 58% of the total and is ranked #1. Second, it has one of the largest proportions of people naturalized 1995 to 1999 at 42% of the total and is ranked #2. The only larger zip code being 37729 with 100%.
ZIP code 37762 Birthplace Demographic Charts
Figure 43 displays a map of the globe and shows ZIP code 37762 demographic information for large regions of the world that people from this place originally came from. 37762 Tennessee has the largest proportion of from Europe at 78% of the total and is ranked #1.
Figure 45 is a table that breaks out all the people who were foreign born by which large geographic region where they were born. 37762 Tennessee has the largest proportion of people who were born in Western Europe at 48.6% of the total and is ranked #1. Second, it has the largest proportion of people who were born in Eastern Europe at 29.7% of the total and is ranked #1.
Zip code 37762, Tennessee Demographics Data
Figure 1: 37762, TN and Area 2019 Population Data
Figure 2: Map of 37762, TN and Area
Figure 3: 37762, TN Population Change 2010 to 2019
Figure 4: 37762, TN 2010 to 2019 Population Percent Change
Figure 5: 37762, TN Population Density
Figure 6: Median Age in 37762, TN
Figure 7: Median Age by Gender in 37762, TN
Figure 8: 37762, TN and Area Age by Generation
Figure 9: 37762, TN and Area Ethnicity Makeup
Figure 10: 37762, TN Hispanic Population
Figure 12: 37762, TN Marriage Status
Figure 14: 37762, TN Average Family Size in Household
Figure 16: Married Couples as Percent of All Families
Figure 17: 37762, TN Head of Household
Figure 18: 37762, TN Birth Rate (Last 12 months)
Figure 20: 37762, TN Teenager Birth Rate
Figure 21: 37762, TN Unwed Mothers as % of All Births
Figure 22: 37762, TN Unwed and On Public Assitance
Figure 24: 37762, TN Unwed Mother Births By Age Group
Figure 25: 37762, TN Unwed Mother Birth Rate By Race
Figure 26: 37762, TN Unwed Mother Births By Poverty Level
Figure 30: 37762, TN Single People Broken Down By Reason
Figure 31: 37762, TN Single Men in Area
Figure 32: 37762, TN Single Women in Area
Figure 35: 37762, TN Citizenship Status
Figure 36: Citizen Place of Birth for 37762, TN
Figure 37: 37762, TN Percent of Population Foreign Born
Figure 39: 37762, TN Non Citizen Age Breakout
Figure 43: 37762, TN Foreign Born People are From What Region
Figure 44: 37762, TN Foreign-Born World Region of Birth
Figure 45: 37762, TN Foreign-Born Sub-Region of Birth
Figure 46: Foreign-Born Country of People Living in 37762, TN
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Digital Signal Processing(DSP) has become a common tool for many fields. These techniques are needed for all departments of natural and social sciences which have data acquisition, analysis and management like engineering physics, chemistry, meteorology, financial, social services and information systems.
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Lisa Ferber is a New York City artist with a non-stop creative output of paintings, plays, short stories, songs and video. In every format she chooses to work in, her creations tend toward the light-hearted and whimsical, celebrating life's eccentrics and a bubbly vision of 20th-century Manhattan. Her screwball plays have been produced at La Mama, the Duplex, and the Brick; her story "Advice From Someone Using" was just published in Three Room Press's new anthology, Have a NYC; and her paintings will be shown this June at the National Arts Club.
She is currently writing and starring in a 1930s-themed Web series, The Sisters Plotz with Lisa Hammer (The Venture Brothers) and Eve Plumb (The Brady Bunch and Love, Loss, and What I Wore on Broadway). The energetic, native New Yorker maintains her ceaseless output on top of working day jobs and leading a full social life. She makes it look easy, even effortless, which can be confusing to those of us who find it hard to do everything we'd like with only 24 hours in a day. Here, she shares three tips for staying prolific and successful that she's learned in launching and sustaining her artistic career.
Explore. Don't feel a need to stick to just one method of self-expression. "You are not just a writer or an artist -- you are a creative person, so don't limit yourself to a particular medium, such as painting, writing, or performing," says Ferber. "I love being multidisciplinary. I see myself and all people as unlimited creative forces. I always create out of joy, whether it's with a pen or a brush or in front of a video camera."
Stay organized. If you want to make a career from your self-expression, you need to take care of the business side of things. "I keep a list of everyone I've sent work to, when I sent it and how they have responded. This way I know what is going on in my life. I also update my arts resume as soon as I have a new accomplishment. I balance between treating my art career like, on the one hand, I'm a six-year-old who just wants to sing and play and put her paintings on her parents' refrigerator, and on the other hand, like I'm running a business."
Find the time -- you do have it. "People often tell me they want to write or make art but that they just can't find the time," says Ferber. "But, when you're passionate, there's no such thing as not having the time. When I started my visual art career, I was also working full-time, plus living with and caring for my elderly mother. What I found is that if you're overworked, making art can revitalize you. Once you stop using time as an excuse, you can figure out what needs to be cut out. Find something that can be cut out just to give yourself a full hour. You owe it to yourself to celebrate that creative world inside you."
Work by Lisa Ferber is currently on view at the Mayson Gallery in New York City. Learn more about Lisa Ferber and her work at www.lisaferber.com.


Going to a natural hair salon has always been on my things-to-do in New York bucket list. I had my fair share of failed regular salon visits, so when I was finally able to get an appointment at one of the premier natural hair salons in New York City, I was beyond ecstatic.
The waiting list for this particular salon was backed up for months, and when I called to make my appointment it just so happened that someone had just cancelled literally 5 minutes prior, so I was in luck. I was so nervous leading up to the day of the appointment and was really trying to figure out what style to get.
I browsed the website and tried to find pricing, but there was none listed. I kind of assumed that it would be expensive, I just didn't know how expensive, but figured I would bite the bullet, since it was on my bucket list.
The day of my appointment, as soon as I walked into the salon I was enamored by all of the natural products in the salon! This salon is a name brand product salon, so all of their products are well known in the natural hair community.
I was really surprised to see products that I've never seen on the shelfs, and made a note to buy something at the end of my appointment. When I finally sat down in my stylist's chair, she spun me around and did a quick assessment of my hair and told me what she thought I needed. She also asked me what style I wanted, and I opted for a simple wash and go.
My wash and gos have been a bit limp lately, so I wanted a professional to work their magic on my curls…. and boy did she! I don't typically tip at salons, but my curls had never been so bouncy and moisturized.
I mean, they were popping! The only downside, I would say is the price. Even before I purchased the products, the bill was over 100 dollars, however I was prepared, and paid. That will probably be me last natural hair salon experience, but it was definitely worth it!
Do you have a fancy salon goal, tag them in the comment section!
Did you wash your hair before you went in for your appointment? I have seen a article that says I should wash my hair before I go so the stylist can see what my hair is like clean with minimal product.
Where in New York City?!


Experts and practioners from across the product management community share their best practices, experiences and success stories. In each presentation, specific issues or problems are highlighted and we ask presenters to focus on key learning points for the audience.
Over 33 speakers will be on stage for Product Management Festival 2017. Stay tuned for more announcements!