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

arXiv: https://arxiv.org/abs/2507.07730 · HTML: https://mlsanity.com/paper/2507.07730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [SegVol: Universal and Interactive Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2311.13385/) (2023)
- [AbdomenAtlas: A Large-Scale, Detailed-Annotated, & Multi-Center Dataset for Efficient Transfer Learning and Open Algorithmic Benchmarking](https://mlsanity.com/paper/2407.16697/) (2024)
- [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025)
- [The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography](https://mlsanity.com/paper/2406.05231/) (2024)
- [RadSAM: Segmenting 3D radiological images with a 2D promptable model](https://mlsanity.com/paper/2504.20837/) (2025)

## Abstract (arXiv)

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.