RAPS-3D: Efficient interactive segmentation for 3D radiological imaging

TL;DR

This paper introduces RAPS-3D, a simplified and efficient promptable segmentation method for 3D medical imaging that reduces inference time and complexity while maintaining state-of-the-art accuracy.

Contribution

The paper proposes a novel 3D segmentation approach inspired by SegVol that simplifies inference and prompt management, improving efficiency over existing methods.

Findings

Achieves state-of-the-art segmentation accuracy on 3D medical datasets.

Reduces inference time compared to traditional 3D segmentation methods.

Eliminates the need for complex prompt management and sliding-window strategies.

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.