how to see the segmentation results after training?

[YilinLiu97]

Hi, this work is great! Do we have to write function ourselves to convert the segmentation results (.mat) to something we can actually see (.nifti)? Thanks.

[josedolz]

Hi, thanks for your nice comments.

Actually you have several options to visualize the results.

• If you have matlab, one option could be to load the segmentation result and show the slice you are interested in. However, this is not the most practical solution in terms of visualization.
• The other option is to use any nifti library (for example nibabel in python or this one in matlab) to create a nifti file from the matlab matrix where results are stored.

Let me know whether that works for you.

Best.

[YilinLiu97]

Hi @josedolz, thanks for the help! This is one of the segmentation result that I visualized using MRIcron after the data format conversion. However, It doesn't look like a segmented image, or did I interpret it wrong? Btw I used the dataset you provided. Thanks!

[josedolz]

Hi,

when you get the segmentation results you have several files. Some of the files are the probability maps of each class before they are assigned to any target label. Then there is one file that contains the classes for the segmentation. I do this because is common today to apply some sort of post-processing to the CNN output probabilities, such as CRF or graph-cuts, to improve the results given by the network.

I just run the example code with the provided dataset and create a nifti image to visualize it (Keep in mind that it is a tinny network running for few iterations, so results are far from being good :) ). Here you can see the segmentation.

[YilinLiu97]

Hi @josedolz, thanks for the reply! I will install the MITK and use it to visualize the result later. Another quick question is, there's no pre-processing steps for applying our own dataset, right? e.i. we don't need to crop it into patches, etc.. I assume this because I saw there was a 'segmentVolume' function in your script.

Updated: I'm able to visualize the segmentation results now. It's beautiful!! Great work!! Btw 1) the MR dataset that you provided for training is from ISBR, right? (And ABIDE just for testing?) Now I'm interested in applying our own datasets and segmenting 25 subcortical structures. 2) One big question is about the pre-processing step mentioned above. 3) Also, why is ROI necessary for training?

It would be great if you have any suggestion on training on custom dataset. Thanks so much!!

[josedolz]

Hi @YilinLiu97, you do not actually need MITK to visualize the output. You can use any visualization tool (MITK, 3D Slicer,and so on). The thing is that you need to get the correct files you want to visualize. To see the final segmentation, the image should be of type int8, and that image looks more like double of float.

Just as a hint, I used this (in Matlab) to create the image in me previous answer:

nii = make_nii(vol, [1 1 1], [0 0 0], [8]);
save_nii('Test.nii',nii);

where vol is the segmentation result. In the demo example, the segmentation result is stored in Segmentation_MR_Img6.mat file.

[YilinLiu97]

Hi, @josedolz, Yes, I used the above codes to do the conversion. It's just that MRIcron doesn't seem to highlight the segmented parts, so I decided to try other softwares, such as MITK, and it turns out well. Thanks for the help!!

[josedolz]

I am glad to hear that you solve that and hope you find the code useful. :)

Regards.

[josedolz]

Btw, for the pre-processing (I forgot it) I typically use public datasets, where images are normally pre-processed. In case they are not, image normalization will often improve your results.

[YilinLiu97]

Got it! Thanks so much! Great work :)