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Semi-supervised Retinal Vessel Segmentation Through Point Consistency

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14437))

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Abstract

Retinal vessels usually serve as biomarkers for early diagnosis and treatment of ophthalmic and systemic diseases. However, collecting and labeling these clinical images require extensive costs and thus existing models are commonly based on extremely limited labeled data for supervised segmentation of retinal vessels, which may hinder the effectiveness of deep learning methods. In this paper, we propose a novel point consistency-based semi-supervised (PCS) framework for retinal vessel segmentation, which can be trained both on annotated and unannotated fundus images. It consists of two modules, one of which is the segmentation module predicting the pixel-wise vessel segmentation map like a common segmentation network. Otherwise, considering that retinal vessels present tubular structures and hence the point set representation enjoys its prediction flexibility and consistency, a point consistency (PC) module is introduced to learn and express vessel skeleton structure adaptively. It inputs high-level features from the segmentation module and produces the point set representation of vessels simultaneously, facilitating supervised segmentation. Meanwhile, we design a consistency regularization between point set predictions and directly predicted segmentation results to explore the inherent segmentation perturbation of the point consistency, contributing to semi-supervised learning. We validate our method on a typical public dataset DRIVE and provide a new large-scale dataset (TR160, including 160 labeled and 120 unlabeled images) for both supervised and semi-supervised learning. Extensive experiments demonstrate that our method is superior to the state-of-the-art methods.

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Acknowledgement

This work was supported by the Beijing Natural Science Foundation under Grant Z200024, in part by Hefei Innovation Research Institute, Beihang University, and in part by the University Synergy Innovation Program of Anhui Province under Grant GXXT-2019-044.

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Authors and Affiliations

  1. School of Biological Science and Medical Engineering, Beihang University, Beijing, China

    Jingfei Hu, Linwei Qiu, Hua Wang & Jicong Zhang

  2. Hefei Innovation Research Institute, Beihang University, Hefei, China

    Jingfei Hu, Linwei Qiu, Hua Wang & Jicong Zhang

  3. Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China

    Jingfei Hu & Jicong Zhang

Authors
  1. Jingfei Hu
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  2. Linwei Qiu
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  3. Hua Wang
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  4. Jicong Zhang
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Corresponding author

Correspondence to Jicong Zhang .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Hu, J., Qiu, L., Wang, H., Zhang, J. (2024). Semi-supervised Retinal Vessel Segmentation Through Point Consistency. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14437. Springer, Singapore. https://doi.org/10.1007/978-981-99-8558-6_13

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  • DOI: https://doi.org/10.1007/978-981-99-8558-6_13

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  • Online ISBN: 978-981-99-8558-6

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