Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

• Published in: Journal of biomedical optics 2018-01, Vol.23 (1), p.015001-015001
• Main Authors: Unger, Jakob, Sun, Tianchen, Chen, Yi-Ling, Phipps, Jennifer E, Bold, Richard J, Darrow, Morgan A, Ma, Kwan-Liu, Marcu, Laura
• Format: Article
• Language: English
• Subjects: Algorithms; Breast Neoplasms - diagnostic imaging; Breast Neoplasms - pathology; Female; Fiducial Markers; Humans; Image Processing, Computer-Assisted - methods; Optical Imaging - methods; Research Papers: General
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.23.1.015001

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.