SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital
  Pathology
Jingwei Zhang Ke Ma Saarthak Kapse Joel Saltz Maria Vakalopoulou Prateek Prasanna Dimitris Samaras
Semantic segmentations of pathological entities have crucial clinical value
in computational pathology workflows. Foundation models, such as the Segment
Anything Model (SAM), have been recently proposed for universal use in
segmentation tasks. SAM shows remarkable promise in instance segmentation on
natural images. However, the applicability of SAM to computational pathology
tasks is limited due to the following factors: (1) lack of comprehensive
pathology datasets used in SAM training and (2) the design of SAM is not
inherently optimized for semantic segmentation tasks. In this work, we adapt
SAM for semantic segmentation by introducing trainable class prompts, followed
by further enhancements through the incorporation of a pathology encoder,
specifically a pathology foundation model. Our framework, SAM-Path enhances
SAM's ability to conduct semantic segmentation in digital pathology without
human input prompts. Through experiments on two public pathology datasets, the
BCSS and the CRAG datasets, we demonstrate that the fine-tuning with trainable
class prompts outperforms vanilla SAM with manual prompts and post-processing
by 27.52% in Dice score and 71.63% in IOU. On these two datasets, the proposed
additional pathology foundation model further achieves a relative improvement
of 5.07% to 5.12% in Dice score and 4.50% to 8.48% in IOU.