Objects with complex structures pose significant challenges to existing
instance segmentation methods that rely on boundary or affinity maps, which are
vulnerable to small errors around contacting pixels that cause noticeable
connectivity change. While the distance transform (DT) makes instance interiors
and boundaries more distinguishable, it tends to overlook the intra-object
connectivity for instances with varying width and result in over-segmentation.
To address these challenges, we propose a skeleton-aware distance transform
(SDT) that combines the merits of object skeleton in preserving connectivity
and DT in modeling geometric arrangement to represent instances with arbitrary
structures. Comprehensive experiments on histopathology image segmentation
demonstrate that SDT achieves state-of-the-art performance.

提出了一种骨架感知的距离变换方法（SDT），其通过将对象骨架和距离变换相结合，以表示具有任意结构的实例，实验证明 SDT 在组织病理学图像分割中实现了最先进的性能。