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深度学习在多序磁共振图像上的自分化,用于腹部上部器官

Asma Amjad¹, Jiaofeng Xu², Dan Thill²

¹Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States.

Frontiers in oncology

|July 24, 2023

概括

这项研究引入了一种多序列深度学习自分割 (mS-DLAS) 模型,用于在放射治疗规划中精确划分器官. 开发的模型准确地对MRI上部腹部器官进行细分,提高了腹部瘤治疗的效率和准确性.

科学领域:

医疗成像医学成像
辐射瘤学辐射瘤学
人工智能的人工智能

背景情况:

多次序MRI对于在放射治疗 (RT) 中定义目标和有风险的器官 (OAR) 是至关重要的.
目前的深度学习自分区模型主要使用单个MRI序列.
需要使用多序MRI数据的先进的自动细分方法.

研究的目的:

开发和评估基于深度学习的多序列自动细分 (mS-DLAS) 模型.
为了利用多序腹部MRI,提高自我细分的准确性.
在放射治疗规划中增强器官和点划分.

主要方法:

一个3DResUnet网络使用71例腹部瘤病例的4个T1和T2权重的MRI序列进行了训练.
实施了数据预处理,Z规范化和数据增强策略.
使用诸如子相似系数 (DSC) 和平均一致距离 (MDA) 等指标评估性能,将ms-DLAS与特定序列模型进行比较.

主要成果:

在mS-DLAS模型中,12个腹部上部器官的平均DSC为0.87和MDA为1.79毫米.
12个腹部上部器官的细分在每例21秒内完成.
该模型通过成功分割其训练套件中不包括的MRI序列来证明其稳定性.

结论:

一个基于MRI的新型mS-DLAS模型已经开发出来,用于自行细分上腹部器官.
多序列细分对于临床RT的准确划分有价值,特别是对于腹部瘤.
这项工作推进了多对比MRI的快速和准确细分,为仅使用MR的放射治疗铺平了道路.

关键词:

在MRI指导的RT中,RT是指导的.腹部器官腹部器官自动细分的自动细分.深度学习是一种深度学习.多个序列的核磁共振成像 (MRI).

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