Relaxation and Restriction for Medical Image Segmentation with Convolutional Neural Networks

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Relaxation and Restriction for Medical Image Segmentation with 
Convolutional Neural Networks"

By

Miss Pei WANG


Abstract

Deep learning has achieved great success in different tasks for natural image 
data in recent years. However, the segmentation of medical image data appears 
to be challenging because of the limited manually labeled data from the medical 
experts, the pathological changes and the morphological variation of the target 
objects, and the random noise associate with the imaging systems. Therefore, 
the effectiveness of convolutional neural networks (CNN) cannot be fully 
achieved. In this thesis, the proposed approaches and techniques are classified 
in two major categories, which are relaxation and restriction methods in CNN 
models to promote the segmentation performance for medical images. For 
multi-modality and multi-class brain tumor segmentation on magnetic resonance 
images, the challenges are the severe data imbalance among the different tumor 
sub-regions, and the great variation in terms of the tumor location, size, 
shape, and appearance. Therefore, to better recognize the overall tumor 
structure, we relax the inner boundary constraints for tumor sub-regions. A 
novel loss function is proposed to automatically enforce more attention on the 
harder classes during training, and a symmetrical attention module is presented 
to restrict the possible location of the predicted tumor. The experimental 
results on the publicly available datasets from real patents validate the 
effectiveness of these proposed approaches.

Colon gland instance segmentation on histological images is a crucial step for 
colorectal cancer diagnosis in clinical practice, but accurate segmentation of 
extremely deformed glands of highly malignant cases or some rare benign cases 
remains to be challenging. Therefore, we relax the input domain to incorporate 
the clinical text for high-level feature guidance of the glandular objects with 
different histologic grades. Besides the initial segmentation, it offers cancer 
grade diagnosis and the enhanced segmentation results for full-scale 
assistance. In the other approach, the gland segmentation is conducted under 
the restriction of hierarchical semantic feature matching from histological 
image pairs in an attentive process, where both spatial details and 
morphological appearances can be well preserved and balanced, especially for 
the glands with severe deformation or mutation.  A loss function is introduced 
to enforce simultaneous satisfaction of semantic correspondence and gland 
instance segmentation on pixel-level. The models successfully boost the 
segmentation performances on the greatly mutated or deformed cases, and 
outperform the state-of-the-art approaches on the public datasets from real 
patients.


Date:			Monday, 31 August 2020

Time:			3:30pm - 5:30pm

Zoom Meeting:		https://hkust.zoom.us/j/4089239113

Chairperson:		Prof. Larry LI (MAE)

Committee Members:	Prof. Albert CHUNG (Supervisor)
 			Prof. Qifeng CHEN
 			Prof. Chiew Lan TAI
 			Prof. Weichuan YU (ECE)
 			Prof. Lin SHI (CUHK)


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