Structure-Preserving Instance Segmentation via Skeleton-Aware Distance
  Transform
Zudi Lin Donglai Wei Aarush Gupta Xingyu Liu Deqing Sun Hanspeter Pfister
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.