训练和评估卷积神经网络在自动血管细分中的性能,使用Ga-68 DOTATATE PET/CT
R Parry1,2, K Wright3, J W Bellinge4,5
1School of Medicine, The University of Western Australia, Perth, Australia. reece.parry@health.wa.gov.au.
The international journal of cardiovascular imaging
|July 5, 2024
概括
使用nnU-Net的人工智能精确评估Ga-68 DOTATATE PET/CT扫描中的血管轮,化和PET标记物吸收. 这种人工智能方法与手动细分相比,大大减少了工作流程时间.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 核医学是一种核医学.
背景情况:
- 在神经内分泌瘤成像中,Ga-68 DOTATATE PET/CT是至关重要的.
- 准确评估血管轮,化和标记物吸收对于诊断和治疗监测至关重要.
- 手动细分是耗时的,并且受观察者之间的变化影响.
研究的目的:
- 评估卷积神经网络 (nnU-Net) 在Ga-68 DOTATATE PET/CT.中细分血管轮,化和PET标记活性方面的性能.
- 将人工智能驱动的细分与经验丰富的观察者的手动细分进行比较.
- 评估AI细分对工作流效率的影响.
主要方法:
- 一个nnU-Net模型被训练,验证,并对116名患者的Ga-68 DOTATATE PET/CT扫描进行了测试.
- 手动心脏和大动脉细分由经验丰富的观察员进行.
- 在手动和人工智能细分之间进行了比较,用于PET追踪剂吸收 (SUVmean) 和评分.
主要成果:
- nnU-Net显示了强烈的正相关性 (r > 0.98) 与血管轮的手动细分.
- 在手动和人工智能细分之间的SUV平均值中,在各种大动脉细分之间没有观察到显著差异.
- 在手动和人工智能测量对PET标记物吸收和血管分数之间发现了良好的一致性 (r ≥ 0.80).
- 与手动细分相比,人工智能细分显著减少了工作流程时间.
结论:
- nnU-Net在Ga-68 DOTATATE PET/CT中提供了血管轮,化和PET标记物吸收的准确和可靠的细分.
- 由人工智能驱动的细分为经验丰富的观察者提供了可比的结果,同时显著提高了工作流效率.
- nnU-Net有望提高Ga-68 DOTATATE PET/CT在神经内分泌瘤评估中的临床实用性.
关键词:
人工智能的人工智能是人工智能.自动细分系统 自动细分系统心血管炎症是一种心血管炎症.冠状动脉疾病是一种冠状动脉疾病.深度学习是一种深度学习.加-68 DOTATATE正子发射断层扫描仪神经网络的神经网络的神经网络更多相关视频
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