在没有MRI成像的情况下,在CT上实现精确的前列腺自分割
Jingwei Duan1, Riley C Tegtmeier2, Carlos E Vargas3
1Mayo Clinic Arizona, Phoenix, AZ, United States; The University of Alabama at Birmingham, Birmingham, AL, United States.
概括
这项研究开发了一种深度学习模型,仅使用CT扫描来准确分离前列腺,克服了MRI的局限性. 该模型实现了MRI级准确性,改善了用于放射治疗的前列腺自身细分.
科学领域:
- 医学成像分析分析 医学成像分析
- 放射治疗计划 放射治疗计划
- 医疗保健中的人工智能
背景情况:
- 磁共振成像 (MRI) 是前列腺细分的黄金标准,但其可访问性有限.
- 基于计算机断层扫描 (CT) 的细分可以高估前列腺体积高达30%与MRI相比,由于观察者偏差.
- 临床资源有限和患者的禁忌限制了全球MRI的可用性.
研究的目的:
- 调查仅使用CT输入实现MRI水平前列腺自分割精度的可行性.
- 开发和评估深度学习 (DL) 模型,使用CT-MRI注册细分训练,以改善前列腺细分.
- 解决在放射治疗中对可访问和准确的前列腺细分的临床需求.
主要方法:
- 一组111名前列腺放射治疗患者的CT和MRI图像被追溯分析.
- 使用CT-MRI注册的细分数据逐步重新训练一个定制的深度学习 (DL) 模型.
- 定制的DL模型在验证数据集上进行了定量评估,医生在验证和测试数据集上进行了定性评估.
主要成果:
- 拟议的DL模型与商业模型相比,表现出卓越的几何相似性,特别是在前列腺顶部区域.
- 与商业基准相比,该模型在子相似系数 (DSC) 和95%的定向豪斯多夫距离 (HD95) 中取得了显著的改进.
- 医生评估表明,69%-78%的模型轮在没有修改的情况下是临床上可接受的.
结论:
- 拟议的增量学习策略通过CT-MRI注册信息来提高前列腺细分的准确性.
- 这种方法可以提高前列腺自分割的准确性,当MRI可用性临床上有限时.
- 开发的DL模型为放射治疗规划中准确和可访问的前列腺细分提供了一个有前途的解决方案.
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