SuperVessel: Segmenting High-Resolution Vessel from Low-Resolution Retinal Image

Yan Hu, Zhongxi Qiu, Dan Zeng, Li Jiang, Chen Lin, Jiang Liu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high-resolution segmentation results for analysis, which leads to super-computational load by most existing methods. If based on low-resolution input, they easily ignore tiny vessels or cause discontinuity of segmented vessels. To solve these problems, the paper proposes an algorithm named SuperVessel, which gives out high-resolution and accurate vessel segmentation using low-resolution images as input. We first take super-resolution as our auxiliary branch to provide potential high-resolution detail features, which can be deleted in the test phase. Secondly, we propose two modules to enhance the features of the interested segmentation region, including an upsampling with feature decomposition (UFD) module and a feature interaction module (FIM) with a constraining loss to focus on the interested features. Extensive experiments on three publicly available datasets demonstrate that our proposed SuperVessel can segment more tiny vessels with higher segmentation accuracy IoU over 6%, compared with other state-of-the-art algorithms. Besides, the stability of SuperVessel is also stronger than other algorithms. The code will be released at

Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision - 5th Chinese Conference, PRCV 2022, Proceedings
EditorsShiqi Yu, Jianguo Zhang, Zhaoxiang Zhang, Tieniu Tan, Pong C. Yuen, Yike Guo, Junwei Han, Jianhuang Lai
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783031189098
Publication statusPublished - 2022
Externally publishedYes
Event5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022 - Shenzhen, China
Duration: 4 Nov 20227 Nov 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13535 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2022


  • Multi-task learning
  • Retinal image
  • Super-resolution
  • Vessel segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


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