Semi-ECNet: Edge-Consistency Based Semi-Supervised Retinal Vessel Segmentation Network

Yilun Qiu1; Zhongxi Qiu1; Yan Hu1; Mingyang Bi1; Yubo Wang1; Jianwen Chen2; Yitian Zhao3; Heng Li1; Jiang Liu1

10.1109/ISBI56570.2024.10635703

2024-05-30

1945-7928

979-8-3503-1334-5

2024 IEEE International Symposium on Biomedical Imaging (ISBI)

27-30 May 2024

Athens, Greece

Blood vessel segmentation plays an important role in the diagnosis and treatment of retinal diseases. The performance of supervised deep-learning-based segmentation methods is dependent on the training labels, which brings a great burden to surgeons. Semi-supervised methods can solve the problem partly, but recently proposed algorithms hardly consider the complexity of the tree structures in retinal images, especially fine peripheral bronchi. Thus, we propose a novel edge-consistency based semi-supervised retinal vessel segmentation algorithm, named Semi-ECNet. Specifically, Semi-ECNet first generates two kinds of vessel maps, including an edge constraint map and a pixel-wise probability map in the model-prediction stage. Then for the loss-consistency stage, we adopt the Sobel operator and propose a novel loss strategy for the consistency constraints among these maps and the ground truth. Extensive experiments on a publicly available dataset demonstrate that our Semi-ECNet effectively leverages unlabeled data, and outperforms other state-of-the-art semi-supervised segmentation methods by introducing this innovative edge-consistency strategy.

第一

1.Department of Computer Science and Engineering, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
2.Southern University of Science and Technology Hospital, Shenzhen, China
3.Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China

Yilun Qiu,Zhongxi Qiu,Yan Hu,et al. Semi-ECNet: Edge-Consistency Based Semi-Supervised Retinal Vessel Segmentation Network[C],2024.