深度学习框架用于从T1-加权MRI图像进行肝脏细分.
Md Sakib Abrar Hossain1,2, Sidra Gul3,4, Muhammad E H Chowdhury2
1NSU Genome Research Institute (NGRI), North South University, Dhaka 1229, Bangladesh.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究开发了一种用于MRI扫描中的肝脏细分的新型级联网络,实现了高精度. 这种机器学习方法有助于计算机辅助诊断肝脏疾病.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是指放射学
背景情况:
- 由于解剖学变异性,在放射图像中肝脏细分具有挑战性.
- 磁共振成像 (MRI) 与CT扫描相比,为肝脏病理诊断提供了优越的软组织对比.
- 由于没有基于Hounsfield单元的预处理,从MRI中自动细分肝脏是很困难的.
研究的目的:
- 在体积MRI中研究肝脏细分的最先进的细分网络.
- 开发和评估一种新型的级联网络,用于从MRI切片中准确地细分肝脏.
主要方法:
- 从CHAOS数据集 (20名患者,647片) 中使用T1加权 (相位) 核磁共振扫描.
- 评估了24个不同的最先进的分段网络与各种编码器/解码器骨干.
- 提出并实施了一种用于轴性肝脏切片分割的新型级联网络架构.
主要成果:
- 拟议的级联网络实现了95.15%的子相似系数 (DSC).
- 该网络获得了92.10%的交叉与联盟 (IoU) 积分.
- 开发的框架在同一个测试组上表现出比现有方法更优异的性能.
结论:
- 这种新的级联网络从MRI中提供了非常精确的肝脏细分.
- 这种自动化方法可以显著提高肝脏疾病的计算机辅助诊断.
- 该方法为基于MRI的肝脏细分任务提供了强大的解决方案.
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