From Registration Uncertainty to Segmentation Uncertainty

arXiv:2403.05111v1 Announce Type: cross
Abstract: 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 uncertainty effectively estimates the potential errors when the registration model is used for label propagation. Here, we propose a novel framework to concurrently …

abstract arxiv cs.cv deep learning eess.iv generated however image process registration research segmentation study transformation type uncertainty understanding