Segment3DBrainTumorUsingDeepLearningExample/BraTs Dataset Train at master · ImageGuidedTherapyLab/Segment3DBrainTumorUsingDeepLearningExample · GitHub

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%fresh start
clc
clear all
close all

gpuDevice(2)


% brats training and validation dataset locations
trImLoc = fullfile('/tmp/BraTS/preprocessedDataset/imagesTr');
valImLoc = fullfile('/tmp/BraTS/preprocessedDataset/imagesVal');

TrLblLoc = fullfile('/tmp/BraTS/preprocessedDataset/labelsTr');
valLblLoc = fullfile('/tmp/BraTS/preprocessedDataset/labelsVal');

 % define readers
maskReader = @(x) (niftiread(x)>0);
volReader = @(x) niftiread(x);
procvolReader = @(x) matRead(x);

%%
% store in datastores
trImgDs = imageDatastore(trImLoc,'FileExtensions','.mat','ReadFcn',procvolReader)
trpxlDs = pixelLabelDatastore(TrLblLoc,["background","tumor"],[0 1],'FileExtensions','.mat', 'ReadFcn',procvolReader )

valImgDs = imageDatastore(valImLoc,'FileExtensions','.mat','ReadFcn',procvolReader)
valpxlDs = pixelLabelDatastore(valLblLoc,["background","tumor"],[0 1],'FileExtensions','.mat','ReadFcn',procvolReader)
%%

% Need Random Patch Extraction on testing and validation Data
patchSize = [57 57 57];
patchPerImage = 25;
miniBatchSize = 8;
  %training patch datastore
trpatchds = randomPatchExtractionDatastore(trImgDs,trpxlDs,patchSize, ...
    'PatchesPerImage',patchPerImage);
trpatchds.MiniBatchSize = miniBatchSize;
  %validation patch datastore
dsVal = randomPatchExtractionDatastore(valImgDs,valpxlDs,patchSize, ...
    'PatchesPerImage',patchPerImage);
dsVal.MiniBatchSize = miniBatchSize;

% before starting, need to define "n" which is the number of channels.
n = 4;

%% paste network layers


%% set training options
options = trainingOptions('adam', ...
    'MaxEpochs',35, ...
    'InitialLearnRate',5e-4, ...
    'LearnRateSchedule','piecewise', ...
    'LearnRateDropPeriod',5, ...
    'LearnRateDropFactor',0.95, ...
    'ValidationData',dsVal, ...
    'ValidationFrequency',400, ...
    'Plots','training-progress', ...
    'Verbose',false, ...
    'MiniBatchSize',miniBatchSize);


%Train
modelDateTime = datestr(now,'dd-mmm-yyyy-HH-MM-SS');
[net,info] = trainNetwork(trpatchds,lgraph,options);
save(['trainedDeepMedic' modelDateTime '-Epoch-' num2str(maxEpochs) '.mat'],'net');