Paper Info
Title
Hierarchical deep network with uncertainty-aware semi-supervised learning for vessel segmentation
Abstract
The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not an easy task since the fine-detailed connected regions of organ vessel bring a lot of ambiguity in vessel segmentation and sub-type recognition, especially for the low-contrast capillary regions. Furthermore, recent two-staged approaches would accumulate and even amplify these inaccuracies from the first-stage whole vessel segmentation into the second-stage sub-type vessel pixel-wise classification. Moreover, the scarcity of manual annotation in organ vessels poses another challenge. In this paper, to address the above issues, we propose a hierarchical deep network where an attention mechanism localizes the low-contrast capillary regions guided by the whole vessels, and enhance the spatial activation in those areas for the sub-type vessels. In addition, we propose an uncertainty-aware semi-supervised training framework to alleviate the annotation-hungry limitation of deep models. The proposed method achieves the state-of-the-art performance in the benchmarks of both retinal artery/vein segmentation in fundus images and liver portal/hepatic vessel segmentation in CT images. Our implementation is publicly available at https://github.com/XGGNet/Vessel-Seg..
Year
DOI
Venue
2022
10.1007/s00521-021-06578-3
NEURAL COMPUTING & APPLICATIONS
Keywords
DocType
Volume
Vessel segmentation, Hierarchical deep network, Attention mechanism, Semi-supervised learning
Journal
34
Issue
ISSN
Citations 
4
0941-0643
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Chenxin Li132.07
Wenao Ma261.79
Liyan Sun373.16
Xinghao Ding459152.95
Yue Huang501.35
Guisheng Wang600.34
Yizhou Yu72907181.26