Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation
This work improves computer-aided diagnosis and surgical planning for medical imaging by enhancing vessel segmentation accuracy, though it is incremental as it builds on existing hierarchical and semi-supervised approaches.
The paper tackles the problem of vessel segmentation and sub-type recognition in medical images, addressing ambiguity in low-contrast capillary regions and annotation scarcity, and achieves state-of-the-art performance on retinal and liver vessel benchmarks.
The analysis of organ vessels is essential for computer-aided diagnosis and surgical planning. But it is not a 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.