  The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich
semantics: capsular invasion, visibility, smoothness, folding and protuberance,
etc. Capsular invasion on tumor boundary has proven to be clinically correlated
with the prognostic indicator, microvascular invasion (MVI). Investigating
tumor boundary semantics has tremendous clinical values. In this paper, we
propose the first and novel computational framework that disentangles the task
into two components: spatial vertex localization and sequential semantic
classification. (1) A HCC tumor segmentor is built for tumor mask boundary
extraction, followed by polar transform representing the boundary with radius
and angle. Vertex generator is used to produce fixed-length boundary vertices
where vertex features are sampled on the corresponding spatial locations. (2)
The sampled deep vertex features with positional embedding are mapped into a
sequential space and decoded by a multilayer perceptron (MLP) for semantic
classification. Extensive experiments on tumor capsule semantics demonstrate
the effectiveness of our framework. Mining the correlation between the boundary
semantics and MVI status proves the feasibility to integrate this boundary
semantics as a valid HCC prognostic biomarker.
