On generalisability of segment anything model for nuclear instance
  segmentation in histology images
Kesi Xu Lea Goetz Nasir Rajpoot
Pre-trained on a large and diverse dataset, the segment anything model (SAM)
is the first promptable foundation model in computer vision aiming at object
segmentation tasks. In this work, we evaluate SAM for the task of nuclear
instance segmentation performance with zero-shot learning and finetuning. We
compare SAM with other representative methods in nuclear instance segmentation,
especially in the context of model generalisability. To achieve automatic
nuclear instance segmentation, we propose using a nuclei detection model to
provide bounding boxes or central points of nu-clei as visual prompts for SAM
in generating nuclear instance masks from histology images.