基准标记
多光谱图像
计算机科学
人工智能
图像配准
金标准(测试)
自体荧光
医学影像学
边距(机器学习)
生物医学工程
计算机视觉
材料科学
光学
放射科
医学
荧光
物理
机器学习
图像(数学)
作者
Jakob Unger,Tianchen Sun,Yi‐Ling Chen,Jennifer E. Phipps,Richard J. Bold
标识
DOI:10.1117/1.jbo.23.1.015001
摘要
An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67 mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.
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