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microsoft / Recursive-Cascaded-Networks

[ICCV 2019] Recursive Cascaded Networks for Unsupervised Medical Image Registration
https://arxiv.org/abs/1907.12353
MIT License
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I used CHAOS CT liver dataset to get bad results #22

Closed zzx332 closed 4 years ago

zzx332 commented 4 years ago

The following is the data after processed. train dataset is 35 ct. valid dataset is 5. final dice score is 0.5

Is there any problem? Is the intensity difference between the images too large? I want to registration between ct and mr.What process should i do with mri image？

微信截图_20200428171011 微信截图_20200428165735
zsyzzsoft commented 4 years ago

This repo has not been tested for cross-modality registration purposes. However, if you want to do that, the first step should be normalizing two modalities (as well as train and val) into similar intensity distributions, or it would be bad results (because of the similarity loss).

zzx332 commented 4 years ago

Thank you for your reply! The auto_liver_mask preprocess in demo.py don't work on my ct dataset,can i crop area around the target object by manual？

zsyzzsoft commented 4 years ago

Yes, a rough crop is fine.

zzx332 commented 4 years ago

In order to register MR to CT.I try to change similarloss to miloss(Mutual information).loss=-miloss+0.1reg_loss Here is my result.(traindata:35ct,35mri,random combination). Mi_loss:https://user-images.githubusercontent.com/45729525/81144615-08e8b400-8fa7-11ea-8c3e-e33d36cf8190.png smooth_loss:https://user-images.githubusercontent.com/45729525/81144674-2584ec00-8fa7-11ea-9b95-d844c5842598.png It looks like miloss is not converging.(decreases too much,from -0.02 to -0.35) smooth_loss first decreases and then increases. Here is dice on the test dataset.It looks ok, but the warped image is bad. val_dice:https://user-images.githubusercontent.com/45729525/81145967-f58b1800-8fa9-11ea-88c4-2643784840e6.png img_fixed:https://user-images.githubusercontent.com/45729525/81146587-2ddf2600-8fab-11ea-9aca-071f6256059e.jpg img_moving:https://user-images.githubusercontent.com/45729525/81146583-2a4b9f00-8fab-11ea-962c-4ec433ff3cd6.jpg img_warped:https://user-images.githubusercontent.com/45729525/81146166-531f6480-8faa-11ea-8130-bada6792eb4f.jpg Is it caused by my loss function setting?

zsyzzsoft commented 4 years ago

This is actually an active research topic... So it is not surprising that some losses may or may not work for this setting.

zzx332 commented 4 years ago

Ok thanks for your answers!