使用卷积神经网络进行MRI序列的元数据独立分类:成功应用于前列腺MRI
Georg L Baumgärtner1, Charlie A Hamm2, Sophia Schulze-Weddige1
1Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Campus Virchow Klinikum, Augustenburgerplatz 1, 13353 Berlin, Germany.
European journal of radiology
|July 15, 2023
概括
一个新的卷积神经网络 (CNN) 准确地分类前列腺MRI序列,仅使用voxel数据. 这种人工智能方法提高了医学成像数据管理和工作流程效率.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 大数据分析大数据分析
背景情况:
- 医疗成像数据量不断增加,这给组织和检索带来了挑战.
- 目前的数据结构缺乏标准化的命名和元数据,以进行强大的图像分析.
- 需要独立于命名和元数据的自动化数据管理.
研究的目的:
- 开发一个卷积神经网络 (CNN) 用于仅基于voxel数据对医学图像进行分类.
- 启用用于医学成像的自动化数据管理,克服命名和元数据的局限性.
- 改善放射学数据的组织,分类和检索.
主要方法:
- 在10个序列类型的3602个前列腺MRI卷上训练了一个3D卷积神经网络 (3D-ResNet18).
- 使用五倍交叉验证与基于患者的分割进行培训和测试.
- 以减少的培训数据评估分类准确性,并对三个外部数据集进行了测试.
主要成果:
- 在分类典型的前列腺MRI序列类型时,获得了99.88% ± 0.13%的准确性.
- 只有10%的训练数据保持了97.43%±2.10%的准确性.
- 在外部验证中,在15种测试的序列类型中,对于10种序列类型的灵敏度证明>90%.
结论:
- 开发的CNN使前列腺MRI中的自动和可靠的序列识别成为可能.
- 基于Voxel的序列识别可以提高医学成像数据管理和工作流程效率.
- 促进了强大的临床AI工作流程,并提高了整体数据质量.
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