基于回声包统计数据的超声参数图像的卷积神经网络分类,用于对肝硬化症的定量诊断
Akiho Isshiki1, Kisako Fujiwara2, Takayuki Kondo2,3
1Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, 263-8522, Japan.
Journal of medical ultrasonics (2001)
|November 23, 2024
概括
这项研究开发了一种使用超声波回声包统计和卷积神经网络来分类脂肪肝等级的方法. 该方法在仅从超声波图像中估计肝脏肥胖症严重性的准确度超过60%.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 肝病学 肝病学是一种肝病学.
背景情况:
- 早期发现肝硬化症对于患者的治疗结果至关重要.
- 对脂肪肝疾病严重程度的定量评估是当前方法的挑战.
研究的目的:
- 用超声波图像研究一种新的方法来分类脂肪肝等级.
- 为了利用回声封面统计 (ES) 和卷积神经网络 (CNN) 进行定量肝硬化症评估.
主要方法:
- 根据MRI-PDFF值,定义了三种脂肪肝等级 (正常,轻度,中度至严重).
- 从ES生成的参数图像,包括第一和第四阶的时刻.
- 使用预训练的VGG-16模型对参数图像中的7680个感兴趣区域 (ROI) 进行了转移学习.
主要成果:
- 在所有参数图像类型中,ROI分类准确率约为46%.
- 从第四阶段的时刻图上对ROI进行硬投票,案例级分类的准确性提高到大约63%.
- 拟议的方法表明了肝硬化症的定量诊断的潜力.
结论:
- 来自ES的参数图像与CNN分类相结合,为肝硬化症诊断提供了定量方法.
- 仅从超声波图像中估计脂肪肝等级,在超过60%的病例中就实现了.
- 这种方法对使用超声波进行肝硬化症的非侵入性定量评估具有前途.
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