Abstract
Belief function theory, a formal framework for uncertainty analysis and multiple evidence fusion, has made significant contributions in the medical domain, especially since the development of deep learning. Medical image segmentation with belief function theory has shown significant benefits in clinical diagnosis and medical image research. In this paper, we provide a review of medical image segmentation methods using belief function theory. We classify the methods according to the fusion step and explain how information with uncertainty or imprecision is modeled and fused with belief function theory. In addition, we discuss the challenges and limitations of present belief function-based medical image segmentation and propose orientations for future research. Future research could investigate both belief function theory and deep learning to achieve more promising and reliable segmentation results.
Abstract (translated)
URL
https://arxiv.org/abs/2205.01733
