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3-D Brain Tumor Segmentation Using Deep Learning

This example shows how to train a 3-D U-Net neural network and perform semantic segmentation of brain tumors from 3-D medical images.

Semantic segmentation involves labeling each pixel in an image or voxel of a 3-D volume with a class. This example illustrates the use of deep learning methods to perform binary semantic segmentation of brain tumors in magnetic resonance imaging (MRI) scans. In this binary segmentation, each pixel is labeled as tumor or background.

This example performs brain tumor segmentation using a 3-D U-Net architecture [1]. U-Net is a fast, efficient and simple network that has become popular in the semantic segmentation domain.

One challenge of medical image segmentation is the amount of memory needed to store and process 3-D volumes. Training a network on the full input volume is impractical due to GPU resource constraints. This example solves the problem by training the network on image patches. The example uses an overlap-tile strategy to stitch test patches into a complete segmented test volume. The example avoids border artifacts by using the valid part of the convolution in the neural network [5].

A second challenge of medical image segmentation is class imbalance in the data that hampers training when using conventional cross entropy loss. This example solves the problem by using a weighted multiclass Dice loss function [4]. Weighting the classes helps to counter the influence of larger regions on the Dice score, making it easier for the network to learn how to segment smaller regions.

Download Training, Validation, and Test Data

This example uses the BraTS data set [2]. The BraTS data set contains MRI scans of brain tumors, namely gliomas, which are the most common primary brain malignancies. The size of the data file is ~7 GB. If you do not want to download the BraTS data set, then go directly to the Download Pretrained Network and Sample Test Set section in this example.

Create a directory to store the BraTS data set.

imageDir = fullfile(tempdir,'BraTS');
if ~exist(imageDir,'dir')

To download the BraTS data, go to the Medical Segmentation Decathlon website and click the "Download Data" link. Download the "Task01_BrainTumour.tar" file [3]. Unzip the TAR file into the directory specified by the imageDir variable. When unzipped successfully, imageDir will contain a directory named Task01_BrainTumour that has three subdirectories: imagesTr, imagesTs, and labelsTr.

The data set contains 750 4-D volumes, each representing a stack of 3-D images. Each 4-D volume has size 240-by-240-by-155-by-4, where the first three dimensions correspond to height, width, and depth of a 3-D volumetric image. The fourth dimension corresponds to different scan modalities. The data set is divided into 484 training volumes with voxel labels and 266 test volumes, The test volumes do not have labels so this example does not use the test data. Instead, the example splits the 484 training volumes into three independent sets that are used for training, validation, and testing.

Preprocess Training and Validation Data

To train the 3-D U-Net network more efficiently, preprocess the MRI data using the helper function preprocessBraTSdataset. This function is attached to the example as a supporting file.

The helper function performs these operations:

  • Crop the data to a region containing primarily the brain and tumor. Cropping the data reduces the size of data while retaining the most critical part of each MRI volume and its corresponding labels.

  • Normalize each modality of each volume independently by subtracting the mean and dividing by the standard deviation of the cropped brain region.

  • Split the 484 training volumes into 400 training, 29 validation, and 55 test sets.

Preprocessing the data can take about 30 minutes to complete.

sourceDataLoc = [imageDir filesep 'Task01_BrainTumour'];
preprocessDataLoc = fullfile(tempdir,'BraTS','preprocessedDataset');

Create Random Patch Extraction Datastore for Training and Validation

Use a random patch extraction datastore to feed the training data to the network and to validate the training progress. This datastore extracts random patches from ground truth images and corresponding pixel label data. Patching is a common technique to prevent running out of memory when training with arbitrarily large volumes.

Create an imageDatastore to store the 3-D image data. Because the MAT-file format is a nonstandard image format, you must use a MAT-file reader to enable reading the image data. You can use the helper MAT-file reader, matRead. This function is attached to the example as a supporting file.

volReader = @(x) matRead(x);
volLoc = fullfile(preprocessDataLoc,'imagesTr');
volds = imageDatastore(volLoc, ...

Create a pixelLabelDatastore to store the labels.

lblLoc = fullfile(preprocessDataLoc,'labelsTr');
classNames = ["background","tumor"];
pixelLabelID = [0 1];
pxds = pixelLabelDatastore(lblLoc,classNames,pixelLabelID, ...

Preview one image volume and label. Display the labeled volume using the labelvolshow function. Make the background fully transparent by setting the visibility of the background label (1) to 0.

volume = preview(volds);
label = preview(pxds);

viewPnl = uipanel(figure,'Title','Labeled Training Volume');
hPred = labelvolshow(label,volume(:,:,:,1),'Parent',viewPnl, ...
    'LabelColor',[0 0 0;1 0 0]);
hPred.LabelVisibility(1) = 0;

Create a randomPatchExtractionDatastore that contains the training image and pixel label data. Specify a patch size of 132-by-132-by-132 voxels. Specify 'PatchesPerImage' to extract 16 randomly positioned patches from each pair of volumes and labels during training. Specify a mini-batch size of 8.

patchSize = [132 132 132];
patchPerImage = 16;
miniBatchSize = 8;
patchds = randomPatchExtractionDatastore(volds,pxds,patchSize, ...
patchds.MiniBatchSize = miniBatchSize;

Follow the same steps to create a randomPatchExtractionDatastore that contains the validation image and pixel label data. You can use validation data to evaluate whether the network is continuously learning, underfitting, or overfitting as time progresses.

volLocVal = fullfile(preprocessDataLoc,'imagesVal');
voldsVal = imageDatastore(volLocVal, ...

lblLocVal = fullfile(preprocessDataLoc,'labelsVal');
pxdsVal = pixelLabelDatastore(lblLocVal,classNames,pixelLabelID, ...

dsVal = randomPatchExtractionDatastore(voldsVal,pxdsVal,patchSize, ...
dsVal.MiniBatchSize = miniBatchSize;

Augment the training and validation data by using the transform function with custom preprocessing operations specified by the helper function augmentAndCrop3dPatch. This function is attached to the example as a supporting file.

The augmentAndCrop3dPatch function performs these operations:

  1. Randomly rotate and reflect training data to make the training more robust. The function does not rotate or reflect validation data.

  2. Crop response patches to the output size of the network, 44-by-44-by-44 voxels.

dataSource = 'Training';
dsTrain = transform(patchds,@(patchIn)augmentAndCrop3dPatch(patchIn,dataSource));

dataSource = 'Validation';
dsVal = transform(dsVal,@(patchIn)augmentAndCrop3dPatch(patchIn,dataSource));

Set Up 3-D U-Net Layers

This example uses the 3-D U-Net network [1]. In U-Net, the initial series of convolutional layers are interspersed with max pooling layers, successively decreasing the resolution of the input image. These layers are followed by a series of convolutional layers interspersed with upsampling operators, successively increasing the resolution of the input image. A batch normalization layer is introduced before each ReLU layer. The name U-Net comes from the fact that the network can be drawn with a symmetric shape like the letter U.

Create a default 3-D U-Net network by using the unetLayers function. Specify two class segmentation. Also specify valid convolution padding to avoid border artifacts when using the overlap-tile strategy for prediction of the test volumes.

inputPatchSize = [132 132 132 4];
numClasses = 2;
[lgraph,outPatchSize] = unet3dLayers(inputPatchSize,numClasses,'ConvolutionPadding','valid');

To better segment smaller tumor regions and reduce the influence of larger background regions, this example uses a dicePixelClassificationLayer. Replace the pixel classification layer with the Dice pixel classification layer.

outputLayer = dicePixelClassificationLayer('Name','Output');
lgraph = replaceLayer(lgraph,'Segmentation-Layer',outputLayer);

The data has already been normalized in the Preprocess Training and Validation Data section of this example. Data normalization in the image3dInputLayer (Deep Learning Toolbox) is unnecessary, so replace the input layer with an input layer that does not have data normalization.

inputLayer = image3dInputLayer(inputPatchSize,'Normalization','none','Name','ImageInputLayer');
lgraph = replaceLayer(lgraph,'ImageInputLayer',inputLayer);

Alternatively, you can modify the 3-D U-Net network by using Deep Network Designer App from Deep Learning Toolbox™.

Plot the graph of the updated 3-D U-Net network.


Specify Training Options

Train the network using the adam optimization solver. Specify the hyperparameter settings using the trainingOptions (Deep Learning Toolbox) function. The initial learning rate is set to 5e-4 and gradually decreases over the span of training. You can experiment with the MiniBatchSize property based on your GPU memory. To maximize GPU memory utilization, favor large input patches over a large batch size. Note that batch normalization layers are less effective for smaller values of MiniBatchSize. Tune the initial learning rate based on the MiniBatchSize.

options = trainingOptions('adam', ...
    'MaxEpochs',50, ...
    'InitialLearnRate',5e-4, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',5, ...
    'LearnRateDropFactor',0.95, ...
    'ValidationData',dsVal, ...
    'ValidationFrequency',400, ...
    'Plots','training-progress', ...
    'Verbose',false, ...

Download Pretrained Network and Sample Test Set

Download a pretrained version of 3-D U-Net and five sample test volumes and their corresponding labels from the BraTS data set [3]. The pretrained model and sample data enable you to perform segmentation on test data without downloading the full data set or waiting for the network to train.

trained3DUnet_url = '';
sampleData_url = '';

imageDir = fullfile(tempdir,'BraTS');
if ~exist(imageDir,'dir')


Train Network

By default, the example loads a pretrained 3-D U-Net network. The pretrained network enables you to run the entire example without waiting for training to complete.

To train the network, set the doTraining variable in the following code to true. Train the model using the trainNetwork (Deep Learning Toolbox) function.

Train on a GPU if one is available. Using a GPU requires Parallel Computing Toolbox™ and a CUDA® enabled NVIDIA® GPU. For more information, see GPU Support by Release (Parallel Computing Toolbox). Training takes about 30 hours on a multi-GPU system with 4 NVIDIA™ Titan Xp GPUs and can take even longer depending on your GPU hardware.

doTraining = false;
if doTraining
    modelDateTime = string(datetime('now','Format',"yyyy-MM-dd-HH-mm-ss"));
    [net,info] = trainNetwork(dsTrain,lgraph,options);
    inputPatchSize = [132 132 132 4];
    outPatchSize = [44 44 44 2];

Perform Segmentation of Test Data

A GPU is highly recommended for performing semantic segmentation of the image volumes (requires Parallel Computing Toolbox™).

Select the source of test data that contains ground truth volumes and labels for testing. If you keep the useFullTestSet variable in the following code as false, then the example uses five volumes for testing. If you set the useFullTestSet variable to true, then the example uses 55 test images selected from the full data set.

useFullTestSet = false;
if useFullTestSet
    volLocTest = fullfile(preprocessDataLoc,'imagesTest');
    lblLocTest = fullfile(preprocessDataLoc,'labelsTest');
    volLocTest = fullfile(imageDir,'sampleBraTSTestSetValid','imagesTest');
    lblLocTest = fullfile(imageDir,'sampleBraTSTestSetValid','labelsTest');
    classNames = ["background","tumor"];
    pixelLabelID = [0 1];

The voldsTest variable stores the ground truth test images. The pxdsTest variable stores the ground truth labels.

volReader = @(x) matRead(x);
voldsTest = imageDatastore(volLocTest, ...
pxdsTest = pixelLabelDatastore(lblLocTest,classNames,pixelLabelID, ...

Use the overlap-tile strategy to predict the labels for each test volume. Each test volume is padded to make the input size a multiple of the output size of the network and compensates for the effects of valid convolution. The overlap-tile algorithm selects overlapping patches, predicts the labels for each patch by using the semanticseg function, and then recombines the patches.

id = 1;
while hasdata(voldsTest)
    disp(['Processing test volume ' num2str(id)]);
    tempGroundTruth = read(pxdsTest);
    groundTruthLabels{id} = tempGroundTruth{1};
    vol{id} = read(voldsTest);
    % Use reflection padding for the test image. 
    % Avoid padding of different modalities.
    volSize = size(vol{id},(1:3));
    padSizePre  = (inputPatchSize(1:3)-outPatchSize(1:3))/2;
    padSizePost = (inputPatchSize(1:3)-outPatchSize(1:3))/2 + (outPatchSize(1:3)-mod(volSize,outPatchSize(1:3)));
    volPaddedPre = padarray(vol{id},padSizePre,'symmetric','pre');
    volPadded = padarray(volPaddedPre,padSizePost,'symmetric','post');
    [heightPad,widthPad,depthPad,~] = size(volPadded);
    [height,width,depth,~] = size(vol{id});
    tempSeg = categorical(zeros([height,width,depth],'uint8'),[0;1],classNames);
    % Overlap-tile strategy for segmentation of volumes.
    for k = 1:outPatchSize(3):depthPad-inputPatchSize(3)+1
        for j = 1:outPatchSize(2):widthPad-inputPatchSize(2)+1
            for i = 1:outPatchSize(1):heightPad-inputPatchSize(1)+1
                patch = volPadded( i:i+inputPatchSize(1)-1,...
                patchSeg = semanticseg(patch,net);
                tempSeg(i:i+outPatchSize(1)-1, ...
                    j:j+outPatchSize(2)-1, ...
                    k:k+outPatchSize(3)-1) = patchSeg;
    % Crop out the extra padded region.
    tempSeg = tempSeg(1:height,1:width,1:depth);

    % Save the predicted volume result.
    predictedLabels{id} = tempSeg;
Processing test volume 1
Processing test volume 2
Processing test volume 3
Processing test volume 4
Processing test volume 5

Compare Ground Truth Against Network Prediction

Select one of the test images to evaluate the accuracy of the semantic segmentation. Extract the first modality from the 4-D volumetric data and store this 3-D volume in the variable vol3d.

volId = 1;
vol3d = vol{volId}(:,:,:,1);

Display in a montage the center slice of the ground truth and predicted labels along the depth direction.

zID = size(vol3d,3)/2;
zSliceGT = labeloverlay(vol3d(:,:,zID),groundTruthLabels{volId}(:,:,zID));
zSlicePred = labeloverlay(vol3d(:,:,zID),predictedLabels{volId}(:,:,zID));

montage({zSliceGT,zSlicePred},'Size',[1 2],'BorderSize',5) 
title('Labeled Ground Truth (Left) vs. Network Prediction (Right)')

Display the ground-truth labeled volume using the labelvolshow function. Make the background fully transparent by setting the visibility of the background label (1) to 0. Because the tumor is inside the brain tissue, make some of the brain voxels transparent, so that the tumor is visible. To make some brain voxels transparent, specify the volume threshold as a number in the range [0, 1]. All normalized volume intensities below this threshold value are fully transparent. This example sets the volume threshold as less than 1 so that some brain pixels remain visible, to give context to the spatial location of the tumor inside the brain.

viewPnlTruth = uipanel(figure,'Title','Ground-Truth Labeled Volume');
hTruth = labelvolshow(groundTruthLabels{volId},vol3d,'Parent',viewPnlTruth, ...
    'LabelColor',[0 0 0;1 0 0],'VolumeThreshold',0.68);
hTruth.LabelVisibility(1) = 0;

For the same volume, display the predicted labels.

viewPnlPred = uipanel(figure,'Title','Predicted Labeled Volume');
hPred = labelvolshow(predictedLabels{volId},vol3d,'Parent',viewPnlPred, ...
    'LabelColor',[0 0 0;1 0 0],'VolumeThreshold',0.68);

hPred.LabelVisibility(1) = 0;

This image shows the result of displaying slices sequentially across the one of the volume. The labeled ground truth is on the left and the network prediction is on the right.

Quantify Segmentation Accuracy

Measure the segmentation accuracy using the dice function. This function computes the Dice similarity coefficient between the predicted and ground truth segmentations.

diceResult = zeros(length(voldsTest.Files),2);

for j = 1:length(vol)
    diceResult(j,:) = dice(groundTruthLabels{j},predictedLabels{j});

Calculate the average Dice score across the set of test volumes.

meanDiceBackground = mean(diceResult(:,1));
disp(['Average Dice score of background across ',num2str(j), ...
    ' test volumes = ',num2str(meanDiceBackground)])
Average Dice score of background across 5 test volumes = 0.9993
meanDiceTumor = mean(diceResult(:,2));
disp(['Average Dice score of tumor across ',num2str(j), ...
    ' test volumes = ',num2str(meanDiceTumor)])
Average Dice score of tumor across 5 test volumes = 0.9585

The figure shows a boxplot (Statistics and Machine Learning Toolbox) that visualizes statistics about the Dice scores across the set of five sample test volumes. The red lines in the plot show the median Dice value for the classes. The upper and lower bounds of the blue box indicate the 25th and 75th percentiles, respectively. Black whiskers extend to the most extreme data points not considered outliers.

If you have Statistics and Machine Learning Toolbox™, then you can use the boxplot function to visualize statistics about the Dice scores across all your test volumes. To create a boxplot, set the createBoxplot variable in the following code to true.

createBoxplot = false;
if createBoxplot
    title('Test Set Dice Accuracy')
    ylabel('Dice Coefficient')


[1] Çiçek, Ö., A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger. "3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation." In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Athens, Greece, Oct. 2016, pp. 424-432.

[2] Isensee, F., P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-Hein. "Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge." In Proceedings of BrainLes: International MICCAI Brainlesion Workshop. Quebec City, Canada, Sept. 2017, pp. 287-297.

[3] "Brain Tumours". Medical Segmentation Decathlon.

The BraTS dataset is provided by Medical Segmentation Decathlon under the CC-BY-SA 4.0 license. All warranties and representations are disclaimed; see the license for details. MathWorks® has modified the data set linked in the Download Pretrained Network and Sample Test Set section of this example. The modified sample dataset has been cropped to a region containing primarily the brain and tumor and each channel has been normalized independently by subtracting the mean and dividing by the standard deviation of the cropped brain region.

[4] Sudre, C. H., W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso. "Generalised Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop. Quebec City, Canada, Sept. 2017, pp. 240-248.

[5] Ronneberger, O., P. Fischer, and T. Brox. "U-Net:Convolutional Networks for Biomedical Image Segmentation." In Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Munich, Germany, Oct. 2015, pp. 234-241. Available at arXiv:1505.04597.

See Also

| | | | | | (Deep Learning Toolbox) | (Deep Learning Toolbox)

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