Author: Zhao, Ruohan
Title: Computer-aided diagnosis of colour retinal imaging
Advisors: You, Jia Jane (COMP)
Degree: Ph.D.
Year: 2022
Subject: Retina -- Diseases -- Diagnosis
Retina -- Diseases -- Imaging
Diagnostic imaging -- Data processing
Hong Kong Polytechnic University -- Dissertations
Department: Department of Computing
Pages: xvi, 100 pages : color illustrations
Language: English
Abstract: Color images in retina that captured by digital fundus reveal the systemic diseases and offer a harmless way to detect retinopathy that will affect the retina. For retinal images, automated segmentation of specific regions can help ophthalmologists screen larger population. The identification of certain lesions and main regions is able to provide analysis of diseases. This thesis firstly presents to investigate the vessel segmentation of retinal images which is substantially useful for diagnosing different ophthalmologic diseases. The second part proposes a novel system for segmenting several lesions of fundus based on the limited data.
Automated vessel segmentation is challenging since the width of retinal vessels could vary in a range. The local intensity and contrast of vessels can be too weak to be detected due to surrounding pathologies of lesions. Instead of current segmentation methods that utilize a vanilla upsample module to retain features for segmentation, our proposed network promotes the U-shape framework in a hierarchical structure by adding a novel multi-scale upsample attention (MSUA) module. An end-to-end nested U-shape framework with innovative attention mechanism is adopted in order to make use of features from cross-layers for discriminating vessels. To further enhance the information flow in the network, the skip connection is replaced with nested connection for feature reuse. Through concatenating mutual connection among multi-stages, the network is able to store rich details of vessels. The cross-testing and separate-testing both demonstrate a state-of-­the-art performance in comparison with other methods.
The retinal lesions for DR diagnosis include red lesions (microaneurysms and haemorrhages) and bright lesions (soft exudates and hard exudates). However, common approaches tend to detect mere one single abnormality. Moreover, it is challenging to adopt a data-driven algorithm to achieve fast and reliable lesions localization with limited annotation. To avoid depending on pixel-wise annotation of pathology, we introduce a novel framework to detect various lesions by training a image-level classification network. Through multi-level features and classification score, the proposed network utilizes attention mechanism that is beneficial of discriminative regions. The classifier is regularized by mixing images with refined labels to promote the sensitivity of implicit objects (e.g. small blobs of microaneurysms). The experiment shows that it obtains promising results in comparison with other weakly-supervised methods.
Rights: All rights reserved
Access: open access

Files in This Item:
File Description SizeFormat 
6120.pdfFor All Users15.17 MBAdobe PDFView/Open


Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show full item record

Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/11640