MOIS-SAM2:以实例为基础的分段任何模型2用于全身MRI中神经纤维瘤的多损伤交互分段
Georgii Kolokolnikov1, Marie-Lena Schmalhofer2, Sophie Götz2
1Institute for Applied Medical Informatics, Institute of Computational Neuroscience, and Center for Biomedical Artificial Intelligence (bAIome), University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg, 20246, Germany.
Computers in biology and medicine
|December 28, 2025
概括
一个新的AI模型,MOIS-SAM2,在1型神经纤维素瘤患者的全身MRI中有效地对神经纤维素瘤进行细分. 这种可扩展的工具通过使用最小的用户输入自动化病变识别来帮助临床工作流程.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 神经纤维瘤类型1 (NF1) 是一种遗传性疾病,导致许多神经纤维瘤 (NFs).
- 全身MRI (WB-MRI) 对于NF检测和监测瘤生长至关重要.
- 在WB-MRI中手动对NF进行细分是耗时的,缺乏可扩展性.
研究的目的:
- 开发一种新的交互式细分模型,用于在WB-MRI中有效和可扩展的NF检测.
- 解决现有方法在精度和可扩展性方面的局限性,用于NF细分.
主要方法:
- 介绍了MOIS-SAM2,这是一个多对象交互式细分模型,扩展了Segment Anything Model 2 (SAM2).
- 使用基于示例的语义传播来转移知识,从被提示到未被提示的损伤.
- 经过119次WB-MRI扫描对84名NF1患者进行了培训和评估,并对各种领域转移场景进行了测试.
主要成果:
- 在域内测试集中,MOIS-SAM2实现了0.60的扫描智能子相似系数 (DSC),表现优于基线模型.
- 该模型在不同MRI场强度和扫描仪供应商之间展示了强大的性能.
- 损伤检测F1分数在0.62到0.78之间,模型与专家之间的协议与专家间的协议相当.
结论:
- MOIS-SAM2在WB-MRI中提供了NF的高效和可扩展的互动细分.
- 该模型显示出强大的概括能力,适合临床整合.
- 该模型和代码是公开可用的,以支持进一步的研究和应用.
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