datastore error in dicom AI classification

hi i get this error i need some help to fix it ?

Error using matlab.io.datastore.ImageDatastore
Files must be a valid string scalar, character vector, cell array of character vectors, string array, or a
matlab.io.datastore.FileSet.

on this row

imds = imageDatastore(dataSource,...
    'IncludeSubfolders',true,...
    'FileExtensions','.dcm',...
    'ReadFcn',@(dcmfile) repmat(uint8(dicomread(dicominfo(dcmfile))),1,1,3));

pxds = pixelLabelDatastore(cellstr(labelData),labelDefs.Name,labelDefs.PixelLabelID,...
'IncludeSubfolders',true,...
'ReadFcn',@readFramesLabels,...
'FileExtensions','.mat');
pxlabeltbl = countEachLabel(pxds)

more over if someone can double check my code i whould realy appriciate it

this is the code

clear; close all; clc
%doTraining ;

try 
    prj = currentProject;
catch
    OpenProject(pwd)
    OpenPart01;
    prj = currentProject;
end
datafolder = fullfile(prj.RootFolder,'Data');
load(fullfile(datafolder,"gTruthMed.mat"))
dataSource = gTruthMed.DataSource.Source;
labelData = gTruthMed.LabelData;
labelDefs = gTruthMed.LabelDefinitions;

imds = imageDatastore(dataSource,...
    'IncludeSubfolders',true,...
    'FileExtensions','.dcm',...
    'ReadFcn',@(dcmfile) repmat(uint8(dicomread(dicominfo(dcmfile))),1,1,3));

pxds = pixelLabelDatastore(cellstr(labelData),labelDefs.Name,labelDefs.PixelLabelID,...
'IncludeSubfolders',true,...
'ReadFcn',@readFramesLabels,...
'FileExtensions','.mat');
pxlabeltbl = countEachLabel(pxds);

[net,info] = trainNetwork(datasource,lgraph,options);

augmenter = imageDataAugmenter('RandXReflection',true,...
    'RandXTranslation',[-10 10], 'RandYTranslation',[-10 10],...
    'RandRotation',[0 180]);
dataSource = pixelLabelImageDatastore(imds,pxds,...
    'DataAugmentation',augmenter);

imtotal = numel(dataSource.Images);
trainidx = floor(imtotal*0.75);
validx = floor(imtotal*0.2);
testidx = floor(imtotal*0.05);
shuffleindx = randperm(imtotal);
trainds = partitionByIndex(datasource,shuffleindx(1:trainidx));
valds = partitionByIndex(dataSource,shuffleindx(trainidx+1:trainidx+validx));
testds = partitionByindex(dataSource,shuffleindx(trainidx+validx+1:end));
bar([numel(trainds.Images),numel(valds.Images),numel(testds.Images)])
title('Partitioned Dataset')
xticklabels({'Training Set','Validation Set','Testing Set'})
ylabel('Number of dicoms')


imageSize = [256 256 3];
numClasses = height(pxlabeltbl);
lgraph = segnetLayers(imageSize,numClasses,'vgg16');

 analyzeNetwork(lgraph)

 freaquency = pxlabeltbl.PixelCount ./ pxlabeltbl.ImagePixelCount;

 bar(1:height(pxlabeltbl),frequency)
 xticklabels(pxlabeltbl.Name); xtickangle(45)
 ylabel('Frequency')
 lgraph.Layers(end)
 pxLayer = dicePixelClassificatoinLayer(...
     'Name','Label');
 lgraph = removeLayers(lgraph, lgraph.Layers(end).Name);
 lgraph = addLayers(lgraph,pxLayer);
 lgraph = connectLayers(lgraph,lgraph.Layers(end-1).Name,'labels');
 lgraph.Layers(end)

 if doTraining
     options = trainingOptions('sgdm',...
         'Momentum',0.9,...
         'InitialLearnRate',0.0002,...
         'L2Regularization',0.0005,...
         'MaxEpochs',100,...
         'MiniBatchSize',4,...
         'Shuffle','every-epoch',...
         'VerboseFrequency',100,...
         'ValidationData',valds,...
         'ValidationPatience',5,...
         'Plots','training-progress',...
         'ExecutionEnvironment','auto');  

     [net,info] = trainNetwork(tain,lgraph,options);
 else
     imshow(fullfile(prj.RootFolder,"HelperFunctions","images","SegnetTrainingProgressPlot.png"))
 end

load mySemSegNetwork.mat
testimds = imageDatastore(testds.Images,...
    'FileExtensions','.dcm',...
    'ReadFcn',@(dcmfile) repmat(unit8(dicomread(dicominfo(dcmfile))),1,1,3));
testpxds = pixelLabelDatastore(testds.PixelLabelData,labelDefs.Name,labelDefs.PixelLabelID,...
    'includeSubfolders',true,...
    'readFcn',@readFramesLabels,...
    'FileExtentions','.mat');

pxdsResults = semanticseg(testimds,net,...
    'WriteLocation',fullfile(datafolder,'Results'),...
    'verbose',true);
[B,imtitle] = overlayRndcontour(testimds,pxdsResults);
imshow(B)
title(imtitle)

metrics = evaluateSemanticSegmentation(pxdsResults,testpxds);
metrics.ClassMetrics
h = heatmap(labelDefs.Name,labelDefs.Name,metrics.NormalizedConfusionMatrix.Variables);

thank you

Answer 1

[Kojiro Saito]

Possibly,

dataSource = gTruthMed.DataSource.Source;

is N ×1 cell array.

Just converting it to "1 x N cell array" with single quatation (') would work.

imds = imageDatastore(dataSource',...