在CT扫描中比较不同数据分区策略,以对受COVID-19影响的区域进行细分
Anne de Souza Oliveira1, Marly Guimarães Fernandes Costa1, João Pedro Guimarães Fernandes Costa2
1R&D Center in Electronic and Information Technology, Federal University of Amazonas, Manaus 69077-000, Brazil.
Diagnostics (Basel, Switzerland)
|January 8, 2025
概括
使用深度神经网络进行自动COVID-19细分的切片策略显示出略高于CT扫描策略的性能. 自动细分的准确性与放射科医生观察者之间的协议相提并论.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肺部疾病 肺部疾病
背景情况:
- 反转录聚合酶链反应 (RT-PCR) 是COVID-19诊断的黄金标准.
- 计算机断层扫描 (CT) 成像对于确认症状患者的诊断至关重要,这些患者的RT-PCR结果为负.
- 在CT扫描中精确细分COVID-19受影响的肺部区域对于诊断和监测至关重要.
研究的目的:
- 用深度神经网络评估CT扫描中COVID-19区域的自动细分方法.
- 为了比较自动细分与手动细分的性能,以及放射科医生之间的观察者间协议.
- 评估不同数据分区策略 (CT扫描与切片) 对细分精度的影响.
主要方法:
- 利用了两个数据集和两个深度神经网络架构,包括U-Net和一个新的架构.
- 实施了手动和自动肺部细分技术.
- 在CT扫描和切片策略下使用Dice指标评估细分性能.
主要成果:
- 自动细分实现了自动肺部细分的73.01% (CT扫描策略) 和84.66% (切片策略) 的Dice指标.
- 在手动肺部细分时,Dice对COVID-19细分的指标为74.47% (CT扫描) 和85.35% (切片).
- 在7个CT扫描的子集中,在自动细分性能和观察者间一致性之间没有发现统计学上显著的差异.
结论:
- 与CT扫描策略相比,切片策略在COVID-19细分方面表现略高.
- 自动细分的性能与放射科医生之间的观察者间协议密切匹配,这表明其临床实用性.
- 深度学习模型显示在CT成像中对COVID-19的自动化分析具有前景.
关键词:
在 COVID-19 疫情中,计算机断层扫描 (CT) 是一种计算机断层扫描.卷积神经网络是一种卷积神经网络.冠状病毒冠状病毒病毒深度学习是一种深度学习.图像细分 图像细分肺 肺 肺 肺 肺 肺 肺 肺 肺放射学 放射学是指放射学更多相关视频
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