Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation
This work addresses the challenge of high annotation costs in medical image segmentation by enhancing interactive methods, though it is incremental as it builds on existing SAM capabilities.
The paper tackled the problem of SAM's vulnerability to prompt forms in interactive medical image segmentation by developing a reinforcement learning framework called TEPed prompts optimization, which improved SAM's zero-shot capability on the BraTS2020 benchmark.
The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high annotation cost, the medical image segmentation (MIS) community has been encouraged to investigate SAM's zero-shot capabilities to facilitate automatic annotation. Inspired by the extraordinary accomplishments of interactive medical image segmentation (IMIS) paradigm, this paper focuses on assessing the potential of SAM's zero-shot capabilities within the IMIS paradigm to amplify its benefits in the MIS domain. Regrettably, we observe that SAM's vulnerability to prompt forms (e.g., points, bounding boxes) becomes notably pronounced in IMIS. This leads us to develop a framework that adaptively offers suitable prompt forms for human experts. We refer to the framework above as temporally-extended prompts optimization (TEPO) and model it as a Markov decision process, solvable through reinforcement learning. Numerical experiments on the standardized benchmark BraTS2020 demonstrate that the learned TEPO agent can further enhance SAM's zero-shot capability in the MIS context.