bailiangj / spine-ct-mr-registration Goto Github PK

Hello, I am reading your article. But I can't find the training and inference script files. I hope you can upload them. Thank you!

Hi BailiangJ,

Thank you for your great MICCAI paper and for making the code public!

I am also working on DL registration and I have a question about the rigid mask.
If I understand correctly, the PC and OC loss in your paper was applied within the bony mask from the moving image:

“Compared to previous supervised DLIR methods, our approach only requires the moving label (MC 1).”

And I have read the paper "A rigidity penalty term for nonrigid registration" by Staring et al, where they also did it on the moving image:

"The rigidity coefficient image only has to be defined on the moving image. The moving image deforms to the fixed image, so specific parts of the moving image should transform in a rigid fashion. Therefore, the regions that correspond to rigid structures have to be defined on the moving image."

However, when I read the voxelmorph paper and the Elastix manual, I personally think the rigidity loss should be applied on the mask from the fixed image. I have attached the evidence below, but in short the key point is that the deformation field is defined in the fixed image domain. If the deformation field is defined in the fixed image domain, I think the rigidity loss should also be defined on the mask from the fixed image (instead of the moving).

That being said, I do see in both Staring's and your paper, the rigidity loss is defined on the moving mask. I am really confused and stuck at this point and I don't know whether or where I made a mistake. Could you please give me any insights on this issue? Thank you so much in advance.

Evidence from voxelmorph and Elastix manual:

1. In this GitHub issue from voxelmorph, the author mentioned:

"When warping image moving to image fixed, the deformation field is in the space of the fixed image -- it tells you how to move the moving image to get to each grid location in the fixed image. For example, to get to location (123, 143) into the fixed space, it might tell you to grab (2.2, 1.3) voxels from the moving image. The reason this is a bit "backwards" is because moving pixels in this way enables us to interpolate from the moving image -- that is, we can get the "intensity value" at location (125.2, 144.3) from the moving image by bilinear interpolation, and move it to (123, 143)."

2. In the Elastix manual:

Nice work. However, I found that it's really tough to collect more than 160 paired CT and MRI images as training data. Thus, I would be greatly appreciated if you could release your trained model.

Hi @BailiangJ, Thanks for code and repo. What if there is no segmentation for training ? Is this repo customized for this case ?
Is it possible to put step wise training process with commands in readme ?

Thanks
Raj