English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Segmentation Anything Model (SAM) has recently emerged as a foundationmodel for addressing image segmentation. Owing to the intrinsic complexity ofmedical images and the high annotation cost, the medical image segmentation(MIS) community has been encouraged to investigate SAM's zero-shot capabilitiesto facilitate automatic annotation. Inspired by the extraordinaryaccomplishments of interactive medical image segmentation (IMIS) paradigm, thispaper focuses on assessing the potential of SAM's zero-shot capabilities withinthe IMIS paradigm to amplify its benefits in the MIS domain. Regrettably, weobserve that SAM's vulnerability to prompt forms (e.g., points, bounding boxes)becomes notably pronounced in IMIS. This leads us to develop a framework thatadaptively offers suitable prompt forms for human experts. We refer to theframework above as temporally-extended prompts optimization (TEPO) and model itas a Markov decision process, solvable through reinforcement learning.Numerical experiments on the standardized benchmark BraTS2020 demonstrate thatthe learned TEPO agent can further enhance SAM's zero-shot capability in theMIS context.

Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation