EviPrompt: A Training-Free Evidential Prompt Generation Method for
  Segment Anything Model in Medical Images
Yinsong Xu Jiaqi Tang Aidong Men Qingchao Chen
Medical image segmentation has immense clinical applicability but remains a
challenge despite advancements in deep learning. The Segment Anything Model
(SAM) exhibits potential in this field, yet the requirement for expertise
intervention and the domain gap between natural and medical images poses
significant obstacles. This paper introduces a novel training-free evidential
prompt generation method named EviPrompt to overcome these issues. The proposed
method, built on the inherent similarities within medical images, requires only
a single reference image-annotation pair, making it a training-free solution
that significantly reduces the need for extensive labeling and computational
resources. First, to automatically generate prompts for SAM in medical images,
we introduce an evidential method based on uncertainty estimation without the
interaction of clinical experts. Then, we incorporate the human prior into the
prompts, which is vital for alleviating the domain gap between natural and
medical images and enhancing the applicability and usefulness of SAM in medical
scenarios. EviPrompt represents an efficient and robust approach to medical
image segmentation, with evaluations across a broad range of tasks and
modalities confirming its efficacy.