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弱监督的实时实例细分用于中枢神经的超声波图像
Tian-Tian Zhang1, Hua Shu2, Zhi-Ri Tang1
1Department of Computer Science, City University of Hong Kong, Hong Kong Special Administrative Region.
Computers in biology and medicine
|June 4, 2023
概括
本研究介绍了CoarseInst,这是一个新的弱监督框架,用于实时超声波图像细分,仅使用框注释. 与完全监督的手腕道诊断和超声波导向区域麻醉方法相比,它实现了卓越的性能.
科学领域:
- 医疗成像医学成像
- 深度学习 (Deep Learning) 是一种深度学习.
- 计算机视觉 计算机视觉
背景情况:
- 医学超声波成像对于超声波导向区域麻醉 (UGRA) 和手腕道综合征 (CTS) 诊断等程序至关重要.
- 基于深度学习的实例细分显示了超声波数据分析的前景,但往往缺乏实时能力,需要大量的手动注释.
研究的目的:
- 为医疗超声波图像开发实时实例细分框架,仅使用框注释.
- 解决完全监督方法在注释工作和实时性能方面的局限性.
主要方法:
- 提议 CoarseInst,一个监管薄弱的框架,具有新的网络结构和两阶段的"粗到细"培训战略.
- 使用框注释进行训练,结合一个对象增强块,放大和通缩损失函数,以及一个中心区域面罩搜索算法.
- 采用一种自我训练阶段,具有新的自我特征相似性损失,以提高面具精度.
主要成果:
- CoarseInst 在超声波图像上实现实时实例细分.
- 该框架在实用的超声波数据集上展示了与最先进的完全监督的方法相比更高的性能.
- 在UGRA和CTS应用中有效地对中枢神经进行细分.
结论:
- CoarseInst提供了一种高效和有效的解决方案,用于实时超声波实例细分,并减少了注释要求.
- 带有框注释的弱监督学习是医学成像中完全监督方法的可行替代方案.
- 拟议的框架具有显著的潜力,可以提高医疗超声波的诊断准确性和程序指导.
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