  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.
