FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images

arXiv:2403.09827v1 Announce Type: cross
Abstract: Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly prompted. Interactivity is a key strength of SAMs, allowing users to iteratively provide prompts that specify objects of interest to refine outputs. However, to realize the interactive use of SAMs for 3D medical imaging tasks, rapid inference times are necessary. High memory requirements and long processing delays remain constraints that hinder …

arxiv cs.cv eess.iv images medical segment segment anything segment anything model type