Medical Image Segmentation with Belief Function Theory and Deep Learning

TL;DR

This paper integrates belief function theory with deep learning to improve medical image segmentation, especially under uncertain, imprecise, and partial information, through novel frameworks and evidence fusion techniques.

Contribution

It introduces a semi-supervised segmentation framework, compares evidential classifiers, and develops a multimodal image fusion method leveraging belief functions.

Findings

Evidential neural networks effectively quantify uncertainty.

Semi-supervised approach reduces annotation-related uncertainty.

Multimodal fusion improves segmentation reliability.

Abstract

Deep learning has shown promising contributions in medical image segmentation with powerful learning and feature representation abilities. However, it has limitations for reasoning with and combining imperfect (imprecise, uncertain, and partial) information. In this thesis, we study medical image segmentation approaches with belief function theory and deep learning, specifically focusing on information modeling and fusion based on uncertain evidence. First, we review existing belief function theory-based medical image segmentation methods and discuss their advantages and challenges. Second, we present a semi-supervised medical image segmentation framework to decrease the uncertainty caused by the lack of annotations with evidential segmentation and evidence fusion. Third, we compare two evidential classifiers, evidential neural network and radial basis function network, and show the effectiveness of belief function theory in uncertainty quantification; we use the two evidential classifiers with deep neural networks to construct deep evidential models for lymphoma segmentation. Fourth, we present a multimodal medical image fusion framework taking into account the reliability of each MR image source when performing different segmentation tasks using mass functions and contextual discounting.