Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth

Martin Halicek, Himar Fabelo, Samuel Ortega, James V. Little, Xu Wang, Amy Y. Chen, Gustavo Marrero Callico, Larry L. Myers, Baran D. Sumer, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Head and neck squamous cell carcinoma (SCCa) is primarily managed by surgical resection. Recurrence rates after surgery can be as high as 55% if residual cancer is present. In this study, hyperspectral imaging (HSI) is evaluated for detection of SCCa in ex-vivo surgical specimens. Several methods are investigated, including convolutional neural networks (CNNs) and a spectral-spatial variant of support vector machines. Quantitative results demonstrate that additional processing and unsupervised filtering can improve CNN results to achieve optimal performance. Classifying regions that include specular glare, the average AUC is increased from 0.73 [0.71, 0.75 (95% confidence interval)] to 0.81 [0.80, 0.83] through an unsupervised filtering and majority voting method described. The wavelengths of light used in HSI can penetrate different depths into biological tissue, while the cancer margin may change with depth and create uncertainty in the ground-truth. Through serial histological sectioning, the variance in cancer-margin with depth is also investigated and paired with qualitative classification heat maps using the methods proposed for the testing group SCC patients.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2019
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsBaowei Fei, Cristian A. Linte
PublisherSPIE
ISBN (Electronic)9781510625495
DOIs
StatePublished - Jan 1 2019
EventMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling - San Diego, United States
Duration: Feb 17 2019Feb 19 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10951
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
CountryUnited States
CitySan Diego
Period2/17/192/19/19

Keywords

  • Convolutional neural network
  • Deep learning
  • Head and neck cancer
  • Head and neck surgery
  • Hyperspectral imaging
  • Intraoperative imaging
  • Optical biopsy

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Halicek, M., Fabelo, H., Ortega, S., Little, J. V., Wang, X., Chen, A. Y., Callico, G. M., Myers, L. L., Sumer, B. D., & Fei, B. (2019). Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. In B. Fei, & C. A. Linte (Eds.), Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling [109511A] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10951). SPIE. https://doi.org/10.1117/12.2512985