VIBESegmentator:为NAKO和英国生物银行提供全身MRI细分
Robert Graf1,2, Paul Platzek3, Evamaria Olga Riedel3
1Department of Diagnostic and Interventional Neuroradiology, School of Medicine, TUM University Hospital Neuro-Kopf-Zentrum Ismaninger Str. 22, 81675, München, Germany. robert.graf@tum.de.
European radiology
|October 9, 2025
概括
我们开发了一个深度学习模型,用于MRI和CT扫描中全面的干细分,实现高精度. 这种公开可用的工具增强了大型研究和临床应用的自动化分析.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 人体解剖学 解剖学 解剖学
背景情况:
- 现有的MRI细分模型缺乏对解剖边界的全面划分.
- 准确的细分对于医学成像中的定量分析至关重要.
研究的目的:
- 提出一个公开可用的深度学习模型,用于MRI和CT的全干细分.
- 为了提供扩展到解剖区间边界的音量智能覆盖.
主要方法:
- 使用TotalSegmentator,脊柱和身体组成模型的初步细分来代改进和重新训练nnUNet模型.
- 培训和验证各种数据集,包括德国国家队列 (NAKO),英国生物库和内部MR/CT数据.
- 71-72结构的细分,包括器官,肌肉,血管,骨和身体组成部件.
主要成果:
- 在一个内部测试中,在71个语义标签的内部测试中,获得了0.90±0.06的平均子得分.
- 与阿莫斯数据集中最好的模型 (Dice 0.81 ± 0.14) 相结合,具有更大的视野和更多结构.
- 在细分广泛的解剖结构方面表现出高精度.
结论:
- 介绍了MRI和CT的公开可用的,准确的全干细分模型.
- 能够进行详细的细分,作为计算机辅助分析的支柱.
- 为临床和研究应用提供精确的身体组成和器官评估.
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