Deformable Registration of Histological Cancer Margins to Gross Hyperspectral Images using Demons

• Type of Material: Article
• Authors: Martin Halicek, Georgia Institute of Technology; James Little III, Emory University; Xu Wang, Emory University; Georgia Chen, Emory University; Mihir Patel, Emory University; Christopher Griffith, Emory University; Mark El-Deiry, Emory University; Nabil Saba, Emory University; Amy Chen, Emory University; Baowei Fei, Emory University
• Language: English
• Date: 2018-01-01
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © COPYRIGHT SPIE.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2293165
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 10581
• Grant/Funding Information: This research is supported in part by NIH grants CA176684, CA156775 and CA204254, Georgia Cancer Coalition Distinguished Clinicians and Scientists Award, and the Developmental Funds from the Winship Cancer Institute of Emory University under award number P30CA138292.
• Keywords: Life Sciences & Biomedicine; head and neck cancer surgery; Science & Technology; Optics; SQUAMOUS-CELL CARCINOMA; Radiology, Nuclear Medicine & Medical Imaging; Demons registration; HEAD; Physical Sciences; TONGUE; digital histology; Hyperspectral imaging; SURGICAL MARGIN
• Research Categories: Health Sciences, Radiology

Abstract

Hyperspectral imaging (HSI), a non-contact optical imaging technique, has been recently used along with machine learning technique to provide diagnostic information about ex-vivo surgical specimens for optical biopsy. The computer-Aided diagnostic approach requires accurate ground truths for both training and validation. This study details a processing pipeline for registering the cancer-normal margin from a digitized histological image to the gross-level HSI of a tissue specimen. Our work incorporates an initial affine and control-point registration followed by a deformable Demons-based registration of the moving mask obtained from the histological image to the fixed mask made from the HS image. To assess registration quality, Dice similarity coefficient (DSC) measures the image overlap, visual inspection is used to evaluate the margin, and average target registration error (TRE) of needle-bored holes measures the registration error between the histologic and HSI images. Excised tissue samples from seventeen patients, 11 head and neck squamous cell carcinoma (HNSCCa) and 6 thyroid carcinoma, were registered according to the proposed method. Three registered specimens are illustrated in this paper, which demonstrate the efficacy of the registration workflow. Further work is required to apply the technique to more patient data and investigate the ability of this procedure to produce suitable gold standards for machine learning validation.