  The microvascular invasion (MVI) is a major prognostic factor in
hepatocellular carcinoma, which is one of the malignant tumors with the highest
mortality rate. The diagnosis of MVI needs discovering the vessels that contain
hepatocellular carcinoma cells and counting their number in each vessel, which
depends heavily on experiences of the doctor, is largely subjective and
time-consuming. However, there is no algorithm as yet tailored for the MVI
detection from pathological images. This paper collects the first pathological
liver image dataset containing 522 whole slide images with labels of vessels,
MVI, and hepatocellular carcinoma grades. The first and essential step for the
automatic diagnosis of MVI is the accurate segmentation of vessels. The unique
characteristics of pathological liver images, such as super-large size,
multi-scale vessel, and blurred vessel edges, make the accurate vessel
segmentation challenging. Based on the collected dataset, we propose an
Edge-competing Vessel Segmentation Network (EVS-Net), which contains a
segmentation network and two edge segmentation discriminators. The segmentation
network, combined with an edge-aware self-supervision mechanism, is devised to
conduct vessel segmentation with limited labeled patches. Meanwhile, two
discriminators are introduced to distinguish whether the segmented vessel and
background contain residual features in an adversarial manner. In the training
stage, two discriminators are devised tocompete for the predicted position of
edges. Exhaustive experiments demonstrate that, with only limited labeled
patches, EVS-Net achieves a close performance of fully supervised methods,
which provides a convenient tool for the pathological liver vessel
segmentation. Code is publicly available at
https://github.com/zju-vipa/EVS-Net.
