Application of belief functions to medical image segmentation: A review

by   Ling Huang, et al.

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.


page 1

page 2

page 3

page 4


Medical Image Segmentation with Belief Function Theory and Deep Learning

Deep learning has shown promising contributions in medical image segment...

A review: Deep learning for medical image segmentation using multi-modality fusion

Multi-modality is widely used in medical imaging, because it can provide...

EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap

Medical image segmentation (MIS) is essential for supporting disease dia...

A Gentle Introduction to Deep Learning in Medical Image Processing

This paper tries to give a gentle introduction to deep learning in medic...

Deep Learning in Multi-organ Segmentation

This paper presents a review of deep learning (DL) in multi-organ segmen...

Improving Uncertainty-based Out-of-Distribution Detection for Medical Image Segmentation

Deep Learning models are easily disturbed by variations in the input ima...

Please sign up or login with your details

Forgot password? Click here to reset