实时深度识别标准化肝脏超声波扫描位置
Jonghwan Shin1, Sukhan Lee2, Juneho Yi1
1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一个深层次的人工智能,可以在超声波图像中自动识别肝脏部分. 这种新系统的准确率超过93%,帮助临床医生诊断肝脏疾病.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肝病学 肝病学是一种肝病学.
背景情况:
- 肝脏超声波 (美国) 对于诊断肝脏疾病至关重要.
- 由于患者的变化和图像的复杂性,在美国图像中准确识别肝脏部分是具有挑战性的.
研究的目的:
- 开发一种自动的实时系统,用于识别标准化的肝脏扫描.
- 通过协调US扫描与参考肝脏部分来指导临床医生.
主要方法:
- 提出了一种新的深层次结构结构,用于将肝脏的图像分为11个标准化扫描.
- 对于模两可的图像,采用了分层分类,每个级别和特征空间都有不同的特征,并进行了近距离分析.
- 实验使用医院获得的美国图像数据集,与不同的患者组进行培训和测试,以评估在变化条件下的表现.
主要成果:
- 拟议的方法获得了超过93%的F1得分,证明了为指导工具的高度充分性.
- 层次架构在准确性方面超过了非层次的方法.
- 该系统有效地处理患者的变化,这是肝脏的关键挑战.
结论:
- 开发的深层次结构架构为自动肝脏扫描分类提供了强大的解决方案.
- 这种人工智能工具可以显著帮助临床医生准确识别和诊断肝脏部分.
- 该方法解决了美国肝脏成像中变异性和复杂性的长期挑战.
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