医疗图像与文本提示的大词汇细分
Ziheng Zhao1,2, Yao Zhang2, Chaoyi Wu1,2
1Shanghai Jiao Tong University, Shanghai, China.
NPJ digital medicine
|September 2, 2025
概括
这项研究介绍了SAT,一种使用文字提示的模型来对3D医学图像进行细分. 与现有方法相比,SAT-Pro表现出更高的性能和通用性.
科学领域:
- 医学成像
- 人工智能
- 计算机视觉
背景情况:
- 3D医学图像的准确细分对于诊断和治疗计划至关重要.
- 现有的模型往往缺乏对不同解剖结构和成像模式的概括能力.
- 使用自然语言进行基于提示的细分是具有重大潜力的新兴领域.
研究的目的:
- 开发一个多功能3D医疗图像细分模型 (SAT),以文本提示为驱动.
- 构建人体解剖学的综合多式知识树和大规模细分数据集.
- 评估拟议的SAT模型 (SAT-Nano和SAT-Pro) 的性能和概括能力.
主要方法:
- 使用6502个解剖学术语构建一个多式知识树.
- 创建一个大规模的3D医学图像细分数据集 (超过22K扫描,497个类别).
- 通过对比学习和文本驱动的细分,开发一个包含医学知识的大词汇细分模型.
主要成果:
- 在497个类别中,SAT-Pro (447M个参数) 的性能与72个专业的nnU-Nets (超过22亿个参数) 相美.
- 在7个人体区域中,SAT-Pro比MedSAM的平均子相似系数 (DSC) 高出7.1%.
- 在外部数据集上,SAT-Pro显示出优异的概括性,比基线平均提高了3.7%.
结论:
- 拟议的SAT模型,特别是SAT-Pro,为3D医疗图像细分提供了强大而可扩展的解决方案.
- 使用医学术语的文本提示细分增强了模型的性能,稳定性和通用性.
- SAT代表了自动化医学图像分析的重大进步,促进了更精确和更有效的临床工作流程.
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