imageDatastore for volumetric images

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Memo Remo
Memo Remo . 2023년 2월 5일
편집: Memo Remo . 2023년 2월 11일
I want to use the imageDatastore command to prepare the training set for training a volumetric convolutional neural network-based semantic segmentation model. I followed the instructions given on the MATLAB webpage below and provided my code with multilayered Tif files representing the input images and the labled input images (ground truth).
However, it seems imageDatastore just reads one of the layers (slices) and cannot process the volumetric images. Does anyone know how we should use this command for the volumetric image segmentation tasks? Or how can we prepare the training set for training a volumetric sementic segmentation model such as Unet 3D ( Many thanks in advance.
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Memo Remo
Memo Remo 2023년 2월 6일
편집: Memo Remo 님. 2023년 2월 6일
Dear Navneet,
Thanks for the reply.
I converted the images to a volume tif file using a MATLAB code that uses the imwrite command with the "append" writemode. I named this file as "Train_Vol".
Then I tried to use the code below to train my model:
Train_imds = blockedImage(Train_Tif_Dir);
classNames = ["L","BT"];
labelIDs = [255 0];
Train_pxds = pixelLabelDatastore(Train_LebelsDir,classNames,labelIDs);
Train_ds = combine(Train_imds,Train_pxds);
net = trainNetwork(Train_ds,lgraph)
lgraph is the model architecture, and Train_LebelsDir is the directory in which I stored the volumetric tif file that contains labels for each training images in the Train_Vol tif file.

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채택된 답변

Ashish Uthama
Ashish Uthama 2023년 2월 7일
이동: Ashish Uthama 님. 2023년 2월 8일
blockedImage cannot convert the slices to a volumetric block on its own unfortunately! blockedImage is useful when you want to break one 'unit' into multiple logical sub-units (i.e one file into many constituting blocks). So its not useful for your workflow at this moment (especially, since it looks like your 'unit' of data will easily fit in memory).
Does this help?
You would replace the first argument of the constructors for imageDatastore and pixellabelDatastore with the list of files you have, their order ought to match to ensure images and labels are paired correctly!.
% Image data
im = uint8(magic(10));
imds = imageDatastore(["vol.tif"],'ReadFcn',@tiffreadVolume);
v = read(imds);
% ans =
% 10 10 2
% Label data
labels = im>50;
lds = pixelLabelDatastore(["labels.tif"],["bg", "fg"], [0 1], 'ReadFcn',@tiffreadVolume);
% Combined data, where one read gives a data,label set:
cds = combine(imds, lds);
d = read(cds)
% d{1} will be image, d{2} will be corresponding labels.
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Memo Remo
Memo Remo 2023년 2월 11일
편집: Memo Remo 님. 2023년 2월 11일
When I want to train the model using this method, I get the following error. I can't understand what is missing here. Do you know how I may check for the error source?
Error using trainNetwork
Invalid or deleted object.
Error in UNet_Mine_V2_CW21_001RICA_TissueNice (line 916)
net = trainNetwork(Train_ds,lgraph,options)
Caused by:
Error using indexing
Invalid or deleted object.

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추가 답변 (1개)

Rylan 2023년 2월 6일
Hi Memo,
The blockedImage object (in Image Processing Toolbox) has documentation that mentions volumes within images:
A blockedImage object is an image made from discrete blocks. Use blocked images when an image or volume is too large to fit into memory. With a blocked image, you can perform processing without running out of memory.
There is an associated datastore which can be used to work with blockedImage called blockedImageDatastore.
Here is a list of examples which use blockedImageDatastore: .
I hope this helps!
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Memo Remo
Memo Remo 2023년 2월 6일
I tried to the ReadFcn you mentioned. Unfortunately, it is not working. It just reads a single slice of the entire volume.
Train_imds = imageDatastore(Tissue_HL_Tif_Dir);
Train_imds.ReadFcn = @tiffreadVolume;
Where "Tissue_HL_Tif_Dir" is the location of volumetric tiff file.

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