计算机科学
分割
人工智能
眼底(子宫)
计算机视觉
保险丝(电气)
模式识别(心理学)
网(多面体)
数学
几何学
工程类
医学
眼科
电气工程
作者
Lin Pan,Zhen Zhang,Shaohua Zheng,Liqin Huang
出处
期刊:Applied sciences
[Multidisciplinary Digital Publishing Institute]
日期:2021-12-31
卷期号:12 (1): 403-403
被引量:8
摘要
Automatic segmentation and centerline extraction of blood vessels from retinal fundus images is an essential step to measure the state of retinal blood vessels and achieve the goal of auxiliary diagnosis. Combining the information of blood vessel segments and centerline can help improve the continuity of results and performance. However, previous studies have usually treated these two tasks as separate research topics. Therefore, we propose a novel multitask learning network (MSC-Net) for retinal vessel segmentation and centerline extraction. The network uses a multibranch design to combine information between two tasks. Channel and atrous spatial fusion block (CAS-FB) is designed to fuse and correct the features of different branches and different scales. The clDice loss function is also used to constrain the topological continuity of blood vessel segments and centerline. Experimental results on different fundus blood vessel datasets (DRIVE, STARE, and CHASE) show that our method can obtain better segmentation and centerline extraction results at different scales and has better topological continuity than state-of-the-art methods.
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