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

2025-07-10

AI Summary

Existing SAM-based methods operate on 2D images and cannot directly process 3D medical volumes (e.g., CT/MRI); moreover, autoregressive slice-wise inference and sliding-window strategies incur high computational overhead, latency, and implementation complexity. To address these limitations, we propose an end-to-end, lightweight 3D prompt-driven segmentation framework built upon the SegVol architecture. Our method introduces a compact 3D prompt encoder and a voxel-wise fully convolutional decoder, enabling direct support for interactive prompts—including points and bounding boxes—without sliding windows or sequential modeling. Evaluated across multiple 3D medical imaging benchmarks, our approach achieves state-of-the-art segmentation accuracy while accelerating inference by 2.1–3.8× and reducing GPU memory consumption by 47%–63%. These improvements significantly enhance real-time performance and clinical usability for interactive 3D medical image segmentation.

Technology Category

• Computer Vision: Segmentation
• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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.

Problem

• Extends 2D promptable segmentation to 3D medical imaging
• Reduces computational complexity in 3D volumetric segmentation
• Simplifies prompt management for interactive 3D segmentation

Innovation

• 3D promptable segmentation for radiological imaging
• Simplified architecture reducing inference time
• Eliminates sliding-window prompt management complexities