使用自由形态变形进行有效的肌肉骨注释
Norio Fukuda1, Shoji Konda1,2, Jun Umehara1,3
1Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), 1-4 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Scientific reports
|July 11, 2024
概括
一个新的工具允许非专家有效地创建用于医学成像的肌肉细分数据集. 这降低了成本,加速了自动细分网络的发展,实现了高精度.
科学领域:
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
- 解剖学建模 解剖学建模
背景情况:
- 用于训练深度学习模型的手动肌肉细分是劳动密集型和昂贵的.
- 当前方法的可扩展性受到专家注释者的需求的限制.
研究的目的:
- 开发和评估一种新型工具,用于有效的,非专家辅助的医学图像注释,用于肌肉细分.
- 评估使用此工具生成的数据集训练的深度学习模型的性能.
主要方法:
- 开发了一个用户友好的工具,使用3D解剖模板模型的自由形式变形.
- 非专家使用该工具来适应模板以定位磁共振图像 (MRI).
- 使用该工具创建的数据集训练了一个自动细分网络.
主要成果:
- 与专家细分相比,非专家注释实现了Dice系数> 0.75.
- 工具辅助的细分显示出最小的错误,例如错误标签或遗漏.
- 用工具生成的数据训练的深度学习模型的表现与用专家数据训练的模型相比或更好.
结论:
- 开发的工具大大减少了创建肌肉细分数据集的时间和劳动力成本.
- 这种方法使医疗图像注释民主化,使临床深度学习应用程序的开发速度更快.
- 该工具可为自动肌肉细分创建高效的数据集,从而有可能改变医学图像分析.
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