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

• Published in: 2024 IEEE International Symposium on Biomedical Imaging (ISBI) pp. 1 - 5
• Main Authors: Qiu, Yilun, Qiu, Zhongxi, Hu, Yan, Bi, Mingyang, Wang, Yubo, Chen, Jianwen, Zhao, Yitian, Li, Heng, Liu, Jiang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.05.2024
• Subjects: Blood vessel segmentation; Blood vessels; Edge-Consistency; Image edge detection; Image segmentation; Retinal image; Retinal vessels; Semi-supervised; Supervised learning; Surgery; Training
• ISSN: 1945-8452
• DOI: 10.1109/ISBI56570.2024.10635703

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.