DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland
  Instance Segmentation
Caiqing Jian Yongbin Qin Lihui Wang
Gland instance segmentation is an essential but challenging task in the
diagnosis and treatment of adenocarcinoma. The existing models usually achieve
gland instance segmentation through multi-task learning and boundary loss
constraint. However, how to deal with the problems of gland adhesion and
inaccurate boundary in segmenting the complex samples remains a challenge. In
this work, we propose a displacement-field assisted graph energy transmitter
(DFGET) framework to solve these problems. Specifically, a novel message
passing manner based on anisotropic diffusion is developed to update the node
features, which can distinguish the isomorphic graphs and improve the
expressivity of graph nodes for complex samples. Using such graph framework,
the gland semantic segmentation map and the displacement field (DF) of the
graph nodes are estimated with two graph network branches. With the constraint
of DF, a graph cluster module based on diffusion theory is presented to improve
the intra-class feature consistency and inter-class feature discrepancy, as
well as to separate the adherent glands from the semantic segmentation maps.
Extensive comparison and ablation experiments on the GlaS dataset demonstrate
the superiority of DFGET and effectiveness of the proposed anisotropic message
passing manner and clustering method. Compared to the best comparative model,
DFGET increases the object-Dice and object-F1 score by 2.5% and 3.4%
respectively, while decreases the object-HD by 32.4%, achieving
state-of-the-art performance.