提出高效3D医学影像交互分割方法，提升精度与速度。
RAPS-3D: Efficient interactive segmentation for 3D radiological imaging
- 基于2D模型思想改进，直接处理3D体数据，无需逐切片推理。
- 相比现有方法显著降低推理时间，保持顶尖分割性能。
- 适合临床快速交互式病灶标注，减轻计算负担。
提示引导分割（promptable segmentation）由图像分割通用模型（SAM）引入，为医学影像提供了临床可交互的分割能力。然而，SAM架构面向2D图像，难以自然扩展至CT或MRI等3D体数据。现有3D方法通常采用自回归策略逐切片传播预测，导致推理复杂度上升；同时为控制内存，常引入滑动窗口等复杂机制，进一步增加推理时长与实现难度。本文提出RAPS-3D，一种受SegVol启发的简化3D提示分割方法，在不依赖滑动窗口的前提下，实现低延迟推理并消除提示管理复杂性，同时达到当前最优性能。
原文摘要 · Abstract (English)
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.
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