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在计算机断层扫描中使用nnU-Net进行自动化关节上棕色脂肪组织细分:与总分离器集成:使用nnU-Net:与总分离器集成

Kasper Jørgensen¹, Frederikke Engel Høi-Hansen¹, Ruth J F Loos²

¹Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark.

Diagnostics (Basel, Switzerland)

|January 8, 2025

概括

这项研究开发了一种使用深度学习对棕色脂肪组织 (BAT) 进行细分的自动化方法,提高了代谢疾病研究的医学成像效率和准确性.

科学领域:

医疗成像医学成像
人工智能的人工智能
代谢疾病代谢疾病

背景情况:

棕色脂肪组织 (BAT) 对能量消耗和温度调节至关重要,使其成为代谢疾病研究的目标.
在医疗图像中手动细分BAT是劳动密集型的,容易变化.

研究的目的:

开发和评估使用nnU-Net深度学习框架的自动棕色脂肪组织 (BAT) 分段方法.
评估这种自动化方法在大量淋巴瘤患者队列中的性能.

主要方法:

在159名淋巴瘤患者的CT扫描中训练了一种3DnnU-Net模型,并使用手动的BAT注释.
创建了一个集合模型并对30名患者的独立队列进行了测试,使用DICE和豪斯多夫距离 (HD) 进行评估.
在BAT中的标准化吸收值 (SUV) 在7107个FDG PET/CT扫描中进行了分析.

主要成果:

整体模型获得了高的DICE分数 (0.780 ± 0.077) 和可接受的HD (29.0 ± 14.6毫米),表现优于单个模型.
自动细分显示了BATSSV的性别,日间时间,季节和年龄的显著差异.
在女性,早晨扫描,冬季和年轻人中观察到较高的BATS SUV.

结论:

自动化的BAT细分工具提供了强大的性能,并减少了手动注释工作.
该工具对大量患者队列的分析验证了已知的BAT SUV模式.
这种方法显示了在代谢疾病研究中广泛的临床和研究应用的潜力.

关键词:

最佳使用方法细分 BAT细分聚乙烯/聚乙烯/聚乙烯整体细分器整体细分器自动化细分的自动化细分.棕色脂肪组织棕色脂肪组织深度学习是一种深度学习.淋巴瘤淋巴瘤是什么在联合国网络上.

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