RAPS-3D: Efficient interactive segmentation for 3D radiological imaging
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
Promptable segmentation, introduced by the Segment Anything Model (SAM), is a
promising approach for medical imaging, as it enables clinicians to guide and
refine model predictions interactively. However, SAM's architecture is designed
for 2D images and does not extend naturally to 3D volumetric data such as CT or
MRI scans. Adapting 2D models to 3D typically involves autoregressive
strategies, where predictions are propagated slice by slice, resulting in
increased inference complexity. Processing large 3D volumes also requires
significant computational resources, often leading existing 3D methods to also
adopt complex strategies like sliding-window inference to manage memory usage,
at the cost of longer inference times and greater implementation complexity. In
this paper, we present a simplified 3D promptable segmentation method, inspired
by SegVol, designed to reduce inference time and eliminate prompt management
complexities associated with sliding windows while achieving state-of-the-art
performance.
