image segmentation using deep learning

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hello guys, hope all doing well
i would like to ask your help in how to train simple deep learning method to segment an image.
i have image, for example 10 image (PNG) for left ventricle of patient from MRI and i have the contours for that images (PNG) . the question is how i can to store the contours as ground truth for the images ? i follow the example below in matlab (traingle segmentation) but for my data i don't know how to store the image contours as ground truth for the training images. if you have any ideas don't hesitate to share with me. thanks a lot
I want to train this network to segment an image corresponding to their contours as shown below:
training image contour for the training image
matlab example:
training image training labeles
dataSetDir = fullfile(toolboxdir('vision'),'visiondata','triangleImages');
imageDir = fullfile(dataSetDir,'trainingImages');
labelDir = fullfile(dataSetDir,'trainingLabels');
imds = imageDatastore(imageDir);
classNames = ["triangle","background"];
labelIDs = [255 0];
pxds = pixelLabelDatastore(labelDir,classNames,labelIDs);
imageSize = [32 32];
numClasses = 2;
lgraph = unetLayers(imageSize, numClasses);
ds = pixelLabelImageDatastore(imds,pxds);
options = trainingOptions('sgdm', ...
'InitialLearnRate',1e-3, ...
'MaxEpochs',50, ...
'VerboseFrequency',10);
net = trainNetwork(ds,lgraph,options);
testImage = imread('triangleTest.jpg');
figure
imshow(testImage)
C = semanticseg(testImage,net);
B = labeloverlay(testImage,C);
figure
imshow(B)

Accepted Answer

Image Analyst
Image Analyst on 27 Dec 2020
You can use the Image Labeler in the Image Processing Toolbox section of the Apps tab on the tool ribbon.
  1 Comment
zakarya shaaf
zakarya shaaf on 27 Dec 2020
i know about image labeler and its really nice tool, but later on i want to use too many images, it will be a tedious task to label them. Whereas that contour already as labels provided by experts. I just want to train that images with thier contours/labels already provided. Thanks a lot for your response.

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