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Deformable registration of histological cancer margins to gross hyperspectral images using demons
Author(s): Martin Halicek; James V. Little; Xu Wang; Zhuo Georgia Chen; Mihir Patel; Christopher C. Griffith; Mark W. El-Deiry; Nabil F. Saba; Amy Y. Chen; Baowei Fei
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Paper 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.

Paper Details

Date Published: 6 March 2018
PDF: 8 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810N (6 March 2018); doi: 10.1117/12.2293165
Show Author Affiliations
Martin Halicek, Georgia Institute of Technology (United States)
Emory Univ. School of Medicine (United States)
Augusta Univ. (United States)
James V. Little, Emory Univ. School of Medicine (United States)
Xu Wang, Emory Univ. School of Medicine (United States)
Zhuo Georgia Chen, Emory Univ. School of Medicine (United States)
Mihir Patel, Emory Univ. School of Medicine (United States)
The Winship Cancer Institute of Emory Univ. (United States)
Christopher C. Griffith, Emory Univ. School of Medicine (United States)
Mark W. El-Deiry, Emory Univ. School of Medicine (United States)
The Winship Cancer Institute of Emory Univ. (United States)
Nabil F. Saba, Emory Univ. School of Medicine (United States)
Amy Y. Chen, Emory Univ. School of Medicine (United States)
The Winship Cancer Institute of Emory Univ. (United States)
Baowei Fei, Georgia Institute of Technology (United States)
Winship Cancer Institute of Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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