Label Inference for Atlas-based Sub-cortical Structure Segmentation in Brain Magnetic Resonance Images

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


PhD Thesis Defence


Title: "Label Inference for Atlas-based Sub-cortical Structure Segmentation in 
Brain Magnetic Resonance Images"

By

Miss Siqi BAO


Abstract

The human brain is a complex neural system composed of several dozen anatomical 
structures. To study the functional and structural properties of its deeper 
sub-cortical regions, three-dimensional image segmentation is a critical step 
in quantitative brain image analysis and clinical diagnosis. However, 
segmenting sub-cortical structures is difficult because they are relatively 
small and have significant shape variations. Moreover, some structure 
boundaries are subtle or even missing in images. Although manual annotation is 
a standard procedure for obtaining quality segmentation, it is time-consuming 
and can suffer from inter- and intra-observer inconsistencies. In recent years, 
researchers have been focusing on developing automatic atlas-based segmentation 
methods incorporating expert prior knowledge about the correspondences between 
intensity profiles and tissue labels. We introduce some novel methods for brain 
MR image segmentation in this thesis, which can be categorized into two main 
parts.

In the first part, several methods relying on non-rigid registration are 
proposed for the label inference of sub-cortical structures in brain MR images. 
A united atlas-based segmentation framework is presented, including forward 
deformation and label refinement. One novel label inference method integrated 
with registration and patch priors is introduced to help correct the label 
errors around structural boundaries. Given the significant overlap of the 
intensity distribution among different tissues, the patch prior based on 
similarity measurement can be adversely impacted. To deal with this problem, a 
new label inference method encoded with local and global patch priors is 
proposed to obtain a more discriminative patch representation.

In the second part, we introduce some advanced label inference methods, which 
don't need the non-rigid registration process with expensive computation. A 
novel network called multi-scale structured CNN is proposed, on top of which 
label consistency is enforced to refine the preliminary results obtained using 
deep learning. With multiple features available for image segmentation, Feature 
Sensitive Label Fusion is presented, which takes the sensitivity among distinct 
features into consideration. To obtain the comprehensive properties for 3D 
brain image, both convolutional LSTM and 3D convolution are employed in the 
Randomized Connection network. Comprehensive experiments have been carried out 
on publicly available datasets and results demonstrate that our methods can 
obtain better performance as compared with other state-of-the-art methods.


Date:			Monday, 10 July 2017

Time:			2:00pm - 4:00pm

Venue:			Room 2612A
 			Lifts 31/32

Chairman:		Prof. Bradley Foreman (PHYS)

Committee Members:	Prof. Albert Chung (Supervisor)
 			Prof. James Kowk
 			Prof. Long Quan
 			Prof. Matthew Mckay (ECE)
 			Prof. Pheng-Ann Heng (CUHK)


**** ALL are Welcome ****