Surgical-DeSAM: Decoupling SAM for Instrument Segmentation in Robotic Surgery

arXiv:2404.14040v1 Announce Type: new
Abstract: Purpose: The recent Segment Anything Model (SAM) has demonstrated impressive performance with point, text or bounding box prompts, in various applications. However, in safety-critical surgical tasks, prompting is not possible due to (i) the lack of per-frame prompts for supervised learning, (ii) it is unrealistic to prompt frame-by-frame in a real-time tracking application, and (iii) it is expensive to annotate prompts for offline applications.
Methods: We develop Surgical-DeSAM to generate automatic bounding box prompts for …

abstract applications arxiv box cs.cv however per performance prompting prompts robotic robotic surgery safety safety-critical sam segment segment anything segment anything model segmentation supervised learning surgery tasks text type