奖章S：面向医学分割的时空文本提示模型

Medal S: Spatio-Textual Prompt Model for Medical Segmentation

November 17, 2025

作者: Pengcheng Shi, Jiawei Chen, Jiaqi Liu, Xinglin Zhang, Tao Chen, Lei Li

cs.AI

摘要

我们推出Medal S——一种支持原生分辨率空间提示与文本提示的端到端可训练医学分割基础模型。与缺乏空间感知的纯文本方法不同，Medal S实现了三维体积提示与文本嵌入的通道级对齐，有效缓解分辨率失配导致的误差。通过保留完整三维上下文，该模型能并行处理多个原生分辨率掩码，显著提升多类别分割性能。轻量化三维卷积模块在双提示引导下实现体素级精度优化，支持BiomedSegFM数据集中CT、MRI、PET、超声及显微影像等模态的243个分割类别。Medal S提供两种提示模式：纯文本模式（以模型预测结果作为空间提示进行自主优化）与混合模式（结合人工标注实现灵活交互）。在24类分割任务中，并行空间提示较顺序提示减少90%以上推理时间。我们提出动态重采样技术解决目标-图像块比例失衡问题，扩展SAT与nnU-Net的数据增强能力，并开发了优化文本预处理、两阶段推理策略及后处理技术以提升内存效率、精度与推理速度。在验证集五模态平均指标上，Medal S以DSC 75.44（对比69.83）、NSD 77.34（对比71.06）、F1 38.24（对比24.88）及DSC TP 65.46（对比46.97）全面超越SAT。该模型通过协调空间精度与语义文本指导，在多类别医学分割任务中展现出较基于顺序提示的方法更优异的效率与准确性。Medal S代码已开源于https://github.com/yinghemedical/Medal-S。

English

We introduce Medal S, a medical segmentation foundation model that supports native-resolution spatial and textual prompts within an end-to-end trainable framework. Unlike text-only methods lacking spatial awareness, Medal S achieves channel-wise alignment between volumetric prompts and text embeddings, mitigating inaccuracies from resolution mismatches. By preserving full 3D context, it efficiently processes multiple native-resolution masks in parallel, enhancing multi-class segmentation performance. A lightweight 3D convolutional module enables precise voxel-space refinement guided by both prompt types, supporting up to 243 classes across CT, MRI, PET, ultrasound, and microscopy modalities in the BiomedSegFM dataset. Medal S offers two prompting modes: a text-only mode, where model predictions serve as spatial prompts for self-refinement without human input, and a hybrid mode, incorporating manual annotations for enhanced flexibility. For 24-class segmentation, parallel spatial prompting reduces inference time by more than 90% compared to sequential prompting. We propose dynamic resampling to address target-patch ratio imbalance, extending SAT and nnU-Net for data augmentation. Furthermore, we develop optimized text preprocessing, a two-stage inference strategy, and post-processing techniques to improve memory efficiency, precision, and inference speed. On the five-modality average on the validation set, Medal S outperforms SAT with a DSC of 75.44 (vs. 69.83), NSD of 77.34 (vs. 71.06), F1 of 38.24 (vs. 24.88), and DSC TP of 65.46 (vs. 46.97). Medal S achieves excellent performance by harmonizing spatial precision with semantic textual guidance, demonstrating superior efficiency and accuracy in multi-class medical segmentation tasks compared to sequential prompt-based approaches. Medal S will be publicly available at https://github.com/yinghemedical/Medal-S.