From Registration Uncertainty to Segmentation Uncertainty

keywords: image registration, registration uncertainty, segmentation uncertainty

This is an official PyTorch implementation of my paper:
Chen, Junyu, et al. "From Registration Uncertainty to Segmentation Uncertainty." Accepted to ISBI 2024.

Understanding the uncertainty inherent in deep learning-based image registration models has been an ongoing area of research. Existing methods have been developed to quantify both transformation and appearance uncertainties related to the registration process, elucidating areas where the model may exhibit ambiguity regarding the generated deformation. However, Our study reveals that neither transformation nor appearance uncertainty effectively estimates the potential errors when the registration model is used for label propagation. Here, we propose a novel framework to concurrently estimate both the epistemic and aleatoric segmentation uncertainties for image registration.

Network architecture:

Segmentation and registration uncertainty estimates:

Quantitative results:

Citation:

If you find this code is useful in your research, please consider to cite:

@article{chen2024registration,
title={From Registration Uncertainty to Segmentation Uncertainty},
author={Chen, Junyu and Liu, Yihao and Wei, Shuwen and Bian, Zhangxing and Carass, Aaron and Du, Yong},
journal={arXiv preprint arXiv:2403.05111},
year={2024}
}