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总细分器MRI:在MRI中对多个解剖结构进行强大的序列独立细分

Tugba Akinci D'Antonoli¹, Lucas K Berger¹, Ashraya K Indrakanti¹

¹Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Petersgraben 4, CH-4031 Basel, Switzerland.

|February 18, 2025

概括

一个新的自动化MRI细分模型,TotalSegmentator MRI,在所有MRI序列中稳定地细分了80个解剖结构. 这种开源工具增强了用于各种临床应用的医学成像分析.

科学领域:

医学成像医学成像
放射学中的人工智能
解剖学细分的细分解剖学细分的细分

背景情况:

对类似于TotalSegmentator CT.的自动化MRI细分工具的需求.
需要一个强大的模型,适用于所有MRI序列和解剖结构.

研究的目的:

开发和评估一个自动化的MRI细分模型,用于对主要解剖结构进行强大的细分,独立于MRI序列.
将TotalSegmentator的功能扩展到MRI扫描中.

主要方法:

一个nnU-Net模型 (TotalSegmentator MRI) 在1143个MRI和CT扫描上受训,以分割80个解剖结构.
使用内部和外部测试集的Dice分数来评估性能,与现有模型和TotalSegmentator CT.相比.
该模型应用于大型数据集,以调查与年龄相关的器官体积变化.

主要成果:

该模型在内部MRI测试套件上获得0.839的Dice得分,超过了其他两个模型.
在TotalSegmentator CT测试组 (子分数0.966) 的表现与TotalSegmentator CT (0.970) 非常接近.
在衰老研究中,年龄和器官体积之间发现了强烈的相关性.

结论:

拟议的开源 TotalSegmentator MRI 模型允许在所有 MRI 序列中自动,强大的细分 80 个结构.
该工具易于使用,并将TotalSegmentator的实用程序扩展到MRI.
现有的在线资源有助于广泛采用和进一步研究.

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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...