Medical Image Segmentation with Belief Function Theory and Deep Learning
Ling Huang
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.