| Title | JOINED: Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection |
| Author | |
| Corresponding Author | Tang, Xiaoying |
| Publication Years | 2022-12-04
|
| Conference Name | Medical Imaging with Deep Learning (MIDL)
|
| Source Title | |
| Pages | 477-492
|
| Conference Date | 6-8July，2022
|
| Conference Place | Zurich, Switzerland
|
| Abstract | Fundus photography has been routinely used to document the presence and severity of
various retinal degenerative diseases such as age-related macula degeneration, glaucoma,
and diabetic retinopathy, for which the fovea, optic disc (OD), and optic cup (OC) are
important anatomical landmarks. Identifification of those anatomical landmarks is of great
clinical importance. However, the presence of lesions, drusen, and other abnormalities
during retinal degeneration severely complicates automatic landmark detection and seg
mentation. Most existing works treat the identifification of each landmark as a single task
and typically do not make use of any clinical prior information. In this paper, we present
a novel method, named JOINED, for prior guided multi-task learning for joint OD/OC
segmentation and fovea detection. An auxiliary branch for distance prediction, in addi
tion to a segmentation branch and a detection branch, is constructed to effffectively utilize
the distance information from each image pixel to landmarks of interest. Our proposed
JOINED pipeline consists of a coarse stage and a fifine stage. At the coarse stage, we obtain
the OD/OC coarse segmentation and the heatmap localization of fovea through a joint seg
mentation and detection module. Afterwards, we crop the regions of interest for subsequent
fifine processing and use predictions obtained at the coarse stage as additional information
for better performance and faster convergence. Experimental results reveal that our pro
posed JOINED outperforms existing state-of-the-art approaches on the publicly-available
GAMMA, PALM, and REFUGE datasets of fundus images. Furthermore, JOINED ranked
the 5th on the OD/OC segmentation and fovea detection tasks in the GAMMA challenge
hosted by the MICCAI2021 workshop OMIA8. |
| Keywords | |
| SUSTech Authorship | First
; Corresponding
|
| Language | English
|
| Data Source | 人工提交
|
| PDF url | https://openreview.net/pdf?id=HU6-t9oKvRW |
| Publication Status | 正式出版
|
| Document Type | Conference paper |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/527496 |
| Department | Department of Electrical and Electronic Engineering |
| Affiliation | 1.Southern University of Science and Technology,Shenzhen, China 2.The University of Hong Kong,Hong Kong, China;Southern University of Science and Technology,Shenzhen, China |
| First Author Affilication | Southern University of Science and Technology |
| Corresponding Author Affilication | Southern University of Science and Technology |
| First Author's First Affilication | Southern University of Science and Technology |
| Recommended Citation GB/T 7714 |
He, Huaqing,Lin, Li,Cai, Zhiyuan,et al. JOINED: Prior Guided Multi-task Learning for Joint Optic Disc/Cup Segmentation and Fovea Detection[C],2022:477-492.
|
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| JOINED Prior Guided （1609KB） | Restricted Access | -- | ||||
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