对带有弱注释的基因病理图像进行端到端同源注册框架
Yuanhua Lin1, Zhendong Liang1, Yonghong He1
1Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, China.
Computer methods and programs in biomedicine
|August 27, 2023
概括
通过ARoNet,即使在初始旋转很大时,也可以实现对组织病理图像的准确同源注册. 这种深度学习框架提高了数字病理学应用的对齐准确度和速度.
科学领域:
- 数字病理学数字病理学
- 生物医学图像分析
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 组织病理学图像注册对于数字病理学至关重要.
- 现有的深度学习方法在病理图像中遇到很大的初始旋转.
- 准确的初始对齐对于有效的图像注册至关重要.
研究的目的:
- 开发一个通用框架,用于对基因病理图像进行端到端的同源注册.
- 为了应对现实世界病理学图像对中的大旋转角度的挑战.
- 为了提高基因病学图像注册的准确性和效率.
主要方法:
- ARoNet框架使用卷积神经网络 (CNN) 来进行特征提取和融合.
- 包含一个旋转识别网络来纠正重要的旋转错位.
- 采用自主监督学习任务,用于无监督的图像表示学习.
主要成果:
- 在对齐准确度方面,ARoNet的表现优于现有的亲缘注册算法,特别是在大型旋转 (例如180度) 时.
- 实现快速执行时间 (每对0.05秒),具有高的注册准确性和稳定性.
- 为随后的非刚性对齐提供准确的亲缘初始化.
结论:
- 该ARoNet框架简化和加快了他的病理图像的注册.
- 在数字病理学中展示了临床应用的潜力.
- 为处理具有挑战性的图像错位提供了强大的解决方案.
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