Medical Image Segmentation with Belief Function Theory and Deep Learning

by   Ling Huang, et al.

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.


page 24

page 25


Application of belief functions to medical image segmentation: A review

Belief function theory, a formal framework for uncertainty analysis and ...

EDC3: Ensemble of Deep-Classifiers using Class-specific Copula functions to Improve Semantic Image Segmentation

In the literature, many fusion techniques are registered for the segment...

Evidence fusion with contextual discounting for multi-modality medical image segmentation

As information sources are usually imperfect, it is necessary to take in...

Real bird dataset with imprecise and uncertain values

The theory of belief functions allows the fusion of imperfect data from ...

RevPHiSeg: A Memory-Efficient Neural Network for Uncertainty Quantification in Medical Image Segmentation

Quantifying segmentation uncertainty has become an important issue in me...

EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap

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

Please sign up or login with your details

Forgot password? Click here to reset