liver tumor image segmentation

hi, I have a CT image 2D and the mask for it. I need to do a MATLAB code for U-NET that I can train for this images and also test images. Can you help me with some ideas? Thank you.

[Image Analyst]

You're welcome. That was easy. Thanks for the announcement. Good luck with it.

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[Yurii Volvenko]

Hi! I have the same task that you had. Did you find the answer to your question? I would really appreciate it if you would share it.

Answer 1

[Coo Boo]

Hi

A sample code as a starting point for training and evaluating a U-Net model:

% Load CT images and their corresponding masks

ct_images = imageDatastore('path to CT images');

masks = imageDatastore('path to masks');

% Resize images and masks to the same size

target_size = [256, 256];

ct_images = augmentedImageDatastore(target_size, ct_images);

masks = augmentedImageDatastore(target_size, masks);

% Split data into training and validation sets

[train_images, val_images] = splitEachLabel(ct_images, 0.8);

[train_masks, val_masks] = splitEachLabel(masks, 0.8);

% Create U-Net model with 4 levels and 64 filters per layer

num_levels = 4;

num_filters = 64;

unet_layers = unetLayers([256, 256, 1], num_filters, 'NumLevels', num_levels);

% Specify training options

opts = trainingOptions('adam', ...

'InitialLearnRate', 1e-4, ...

'MiniBatchSize', 16, ...

'MaxEpochs', 50, ...

'ValidationData', {val_images, val_masks}, ...

'ValidationFrequency', 10, ...

'Plots', 'training-progress');

% Train the U-Net model

trained_unet = trainNetwork(train_images, train_masks, unet_layers, opts);

% Make predictions on test images

test_images = imageDatastore('path to test images');

test_images = augmentedImageDatastore(target_size, test_images);

test_masks = predict(trained_unet, test_images);

% Evaluate performance of U-Net model

metrics = evaluateSemanticSegmentation(test_masks, test_masks);