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图像共记录方法在血管系统的光声学介视镜中的性能评估
Thierry L Lefebvre1, Paul W Sweeney2, Janek Grohl3
1Department of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge, CB3 0HE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Physics in medicine and biology
|September 25, 2024
概括
精确的共记录光声学测声镜图像对于跟踪瘤血管变化至关重要. 局部网深度学习优于其他方法,使得精确的纵向监测瘤血管生成和治疗反应.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 摄影声学 摄影声学
- 医学图像分析 医学图像分析
背景情况:
- 功能性瘤血管系统对于生长和治疗疗效至关重要.
- 中视镜光声学成像允许纵向血管网络的表征.
- 准确的图像联合注册对于评估治疗诱导的血管变化至关重要,但由于信号复杂性和生物可变性,具有挑战性.
研究的目的:
- 为了比较五种开源的共注册方法,用于光声介视镜.
- 评估在生理和病理组织中不断发展的血管网络的纵向对齐.
- 为在瘤中进行定量血管分析的联合注册算法的选择提供信息.
主要方法:
- 从小鼠耳朵和乳腺癌异种移植的3D血管图像是使用光声学介视镜获得的.
- 图像经过预处理并使用无监督的生成对抗网络进行细分.
- 应用了五种联合注册方法:基于亲属强度 (MI,NCC),基于亲属形状 (NCC在距离转换细分上,ICP) 和基于深度学习 (LocalNet).
主要成果:
- 基于强度的相互信息 (MI) 和标准化交叉相关性 (NCC) 方法显示出比基于形状的方法更好的对齐.
- 使用深度学习方法的LocalNet实现了卓越的共同注册准确性,特别是在纵向乳腺癌异种移植中.
- 通过优化子场变形,LocalNet最大限度地减少了目标注册错误,超过了所有其他评估方法的性能.
结论:
- 证明了使用光声介视镜进行纵向成像血管网络的可行联合注册.
- 这项研究代表了对复杂血管结构的量化,纵向表征的一步.
- 开发的工具有助于监测瘤血管生成和量化治疗诱导的血管变化在中等尺度.
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