Application of belief functions to medical image segmentation: A review
It addresses uncertainty in medical image segmentation for risk-critical applications, but is incremental as it reviews existing methods.
This paper reviews the application of belief function theory to medical image segmentation, focusing on how it models and fuses uncertain information, and discusses challenges and future research directions.
The investigation of uncertainty is of major importance in risk-critical applications, such as medical image segmentation. Belief function theory, a formal framework for uncertainty analysis and multiple evidence fusion, has made significant contributions to medical image segmentation, especially since the development of deep learning. In this paper, we provide an introduction to the topic 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.