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

2 Citations (Scopus)

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

Fingerprint

Tumors
margins
tumors
cancer
Neural networks
Glare
evaluation
Surgery
Support vector machines
Neoplasms
Residual Neoplasm
Politics
Tissue
Wavelength
Uncertainty
Area Under Curve
Squamous Cell Carcinoma
glare
Testing
voting

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

Cite this

Halicek, M., Fabelo, H., Ortega, S., Little, J. V., Wang, X., Chen, A. Y., ... 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

Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. / Halicek, Martin; Fabelo, Himar; Ortega, Samuel; Little, James V.; Wang, Xu; Chen, Amy Y.; Callico, Gustavo Marrero; Myers, Larry L.; Sumer, Baran D.; Fei, Baowei.

Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. ed. / Baowei Fei; Cristian A. Linte. SPIE, 2019. 109511A (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10951).

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

Halicek, M, Fabelo, H, Ortega, S, Little, JV, Wang, X, Chen, AY, Callico, GM, Myers, LL, Sumer, BD & Fei, B 2019, Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. in B Fei & CA 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, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, San Diego, United States, 2/17/19. https://doi.org/10.1117/12.2512985
Halicek M, Fabelo H, Ortega S, Little JV, Wang X, Chen AY et al. Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. In Fei B, Linte CA, editors, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. SPIE. 2019. 109511A. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). https://doi.org/10.1117/12.2512985
Halicek, Martin ; Fabelo, Himar ; Ortega, Samuel ; Little, James V. ; Wang, Xu ; Chen, Amy Y. ; Callico, Gustavo Marrero ; Myers, Larry L. ; Sumer, Baran D. ; Fei, Baowei. / Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth. Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling. editor / Baowei Fei ; Cristian A. Linte. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).
@inproceedings{f5a05bdfb21444f78db3aaaf4ad3bd52,
title = "Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth",
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.",
keywords = "Convolutional neural network, Deep learning, Head and neck cancer, Head and neck surgery, Hyperspectral imaging, Intraoperative imaging, Optical biopsy",
author = "Martin Halicek and Himar Fabelo and Samuel Ortega and Little, {James V.} and Xu Wang and Chen, {Amy Y.} and Callico, {Gustavo Marrero} and Myers, {Larry L.} and Sumer, {Baran D.} and Baowei Fei",
year = "2019",
month = "1",
day = "1",
doi = "10.1117/12.2512985",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Baowei Fei and Linte, {Cristian A.}",
booktitle = "Medical Imaging 2019",

}

TY - GEN

T1 - Cancer detection using hyperspectral imaging and evaluation of the superficial tumor margin variance with depth

AU - Halicek, Martin

AU - Fabelo, Himar

AU - Ortega, Samuel

AU - Little, James V.

AU - Wang, Xu

AU - Chen, Amy Y.

AU - Callico, Gustavo Marrero

AU - Myers, Larry L.

AU - Sumer, Baran D.

AU - Fei, Baowei

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Convolutional neural network

KW - Deep learning

KW - Head and neck cancer

KW - Head and neck surgery

KW - Hyperspectral imaging

KW - Intraoperative imaging

KW - Optical biopsy

UR - http://www.scopus.com/inward/record.url?scp=85068897897&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068897897&partnerID=8YFLogxK

U2 - 10.1117/12.2512985

DO - 10.1117/12.2512985

M3 - Conference contribution

AN - SCOPUS:85068897897

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2019

A2 - Fei, Baowei

A2 - Linte, Cristian A.

PB - SPIE

ER -