Labelled data from volume segmenter

I have a 3D data 461by951by651 as a mat file and I used the volume segmenter app to label it. I got a 3D categorical Label of the same size as the data. I can not use this label as an input for a data store or for a training network as it has to be in an array. I tried using reshape function but it didn’t work. Any help?

[Walter Roberson]

reshape the data and the labels, both to 461 by (951 times 651) ?

[AMEN BARGEES]

data and labels are now 461by619101 in 2 separate .mat files,

do I have to save it as 461by1 619101 times files for the data and again for the labels as when I try the datastore splitEachLabel gives no validation files which give the error below.

imds=imageDatastore("F3\Data","FileExtensions",".mat","LabelSource","none", ...
    "ReadFcn",@matRead,"Labels","F3\Labels");
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomize');
inputSize = [10 10];
numClasses = 2;

layers = [
    imageInputLayer(inputSize)
    convolution2dLayer(5,20)
    batchNormalizationLayer
    reluLayer
    fullyConnectedLayer(numClasses)
    softmaxLayer
    classificationLayer];
options = trainingOptions('sgdm', ...
    'MaxEpochs',4, ...
    'ValidationData',imdsValidation, ...
    'ValidationFrequency',30, ...
    'Verbose',false, ...
    'Plots','training-progress');
net = trainNetwork(imdsTrain,layers,options);

Error using nnet.cnn.TrainingOptionsSGDM (line 119)

The value of 'ValidationData' is invalid. ImageDatastore has no labels.

Error in trainingOptions (line 324)

opts = nnet.cnn.TrainingOptionsSGDM(varargin{:});

[Walter Roberson]

'Labels' as a property is expected to be the actual label content. There is no direct way to specify a corresponding file name, not that I can see.

You can set the Labels property, but you cannot do so on a per-file basis.

When you use imageDatastore, then the label is expected to be the label for the entire image.

https://www.mathworks.com/support/search.html/videos/3d-image-segmentation-of-brain-tumors-using-deep-learning-1566209336897.html

Perhaps you should be using Ground Truth techniques; https://www.mathworks.com/help/vision/ref/groundtruth.html and https://www.mathworks.com/discovery/ground-truth.html