Medal S：面向医学分割的空间-文本提示模型
Medal S: Spatio-Textual Prompt Model for Medical Segmentation
AI总结：
本文提出医学分割基础模型Medal S，通过融合原生分辨率空间与文本提示、配套优化策略，在多模态多类分割任务上实现了优于现有方法的精度与推理效率。
摘要：
我们推出Medal S，这是一款医学分割基础模型，在端到端可训练框架内支持原生分辨率的空间提示与文本提示。与缺乏空间感知能力的纯文本方法不同，Medal S实现了体素提示与文本嵌入之间的通道级对齐，缓解了分辨率不匹配带来的误差。通过保留完整的3D上下文，该模型可并行处理多个原生分辨率掩码，提升了多类别分割性能。轻量级3D卷积模块可在两类提示的引导下实现精确的体素空间细化，支持BiomedSegFM数据集中CT、MRI、PET、超声、显微镜五种模态下最多243个类别的分割任务。Medal S提供两种提示模式：纯文本模式下无需人工输入，模型预测结果可作为空间提示完成自细化；混合模式则可整合人工标注，提升使用灵活性。针对24类分割任务，并行空间提示相比顺序提示可将推理时间降低90%以上。我们提出动态重采样方法解决目标-块比例不平衡问题，扩展了SAT与nnU-Net用于数据增强。此外，我们开发了优化的文本预处理、两阶段推理策略以及后处理技术，以提升内存效率、精度与推理速度。在验证集的五种模态平均结果上，Medal S表现优于SAT，其DSC为75.44（对比SAT的69.83）、NSD为77.34（对比71.06）、F1为38.24（对比24.88）、DSC TP为65.46（对比46.97）。Medal S通过协调空间精度与语义文本引导实现了优异性能，相比基于顺序提示的方法，在多类别医学分割任务中展现出更出色的效率与精度。Medal S将在https://github.com/yinghemedical/Medal-S公开可用。
英文摘要：
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.
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