RETINAL VESSEL SEGMENTATION WITH DEEP LEARNING TECHNIQUES

PhD Thesis Proposal Defence


Title: "RETINAL VESSEL SEGMENTATION WITH DEEP LEARNING TECHNIQUES"

by

Mr. Yishuo ZHANG


Abstract:

The retinal vasculature is the only vascular system that can be viewed in a 
non-invasive manner and the characteristics of the retinal vascular offer 
diagnostic information. Therefore, it plays an important role in ophthalmology 
examination and is commonly studied in the research community. Developing 
automatic retinal vessel segmentation methods from retinal images is in urgent 
need, however, this task is difficult. The main obstacles include poor image 
quality, tiny structures of vessels, and various abnormalities, which increase 
difficulties in the retinal vessels segmentation. In recent years, deep 
learning techniques have emerged and led to promising progress for retinal 
vessel segmentation. However, this task is not fully solved. The unsolved 
issues include: current methods usually fail to make precise predictions for 
micro-vascular; predictions from CNNs based methods have a unsatisfactory 
graphical structure compared with the ground truth; learning-based methods 
highly rely on perfect annotations and flawed annotations can lead to bad 
performance.

In this study, we further explore deep learning techniques for retinal vessel 
segmentation and focus on the issues mentioned above. First, we propose a U-Net 
based method with deep supervision to preserve spatial information in deep 
layers. We specifically design a vessel-dependent loss to deal with different 
errors in cases of thick vessels and thin vessels. The proposed method provides 
better performance on vessel segmentation. Second, we design a novel method for 
exploring graphical structures of vessels by graph convolutional networks, 
which further improved the performance of vessel segmentation. The proposed 
method produces vessels with improved graphical structures. Third, we proposed 
a Transformer-U-Net hybrid model which improves segmentation results by a 
global receptive field. We also mitigate an imbalance issue between thin 
vessels and thick vessels by a skeleton-aware decoder, in which the model 
learns the skeleton of vessels as an auxiliary task. The proposed method has 
been evaluated in different imaging modalities and achieve promising 
performances. Fourth, we present a robust learning scheme under the condition 
of wrong annotations. A self-attention module with a label adaptation scheme 
increases the robustness toward label noise and enables learning with imperfect 
annotations. In conclusion, this study will not only provide cutting-edge 
techniques for retinal vessel segmentation but also offer insights into the 
segmentation of tiny tubular structures.


Date:			Friday, 6 May 2022

Time:                  	2:00pm - 4:00pm

Zoom 
Meeting:https://hkust.zoom.us/j/93036118132?pwd=MXBNTUlnaE1qQ1hSeDNWUXFhTTdadz09

Committee Members:	Prof. Albert Chung (Supervisor)
  			Prof. Chi-Keung Tang (Supervisor)
 			Prof. Chiew-Lan Tai (Chairperson)
 			Prof. Pedro Sander


**** ALL are Welcome ****