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Proceedings Paper

Hyperspectral microscopic imaging for automatic detection of head and neck squamous cell carcinoma using histologic image and machine learning
Author(s): Ling Ma; Martin Halicek; Ximing Zhou; James Dormer; Baowei Fei
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Paper Abstract

The purpose of this study is to develop hyperspectral imaging (HSI) for automatic detection of head and neck cancer cells on histologic slides. A compact hyperspectral microscopic system is developed in this study. Histologic slides from 15 patients with squamous cell carcinoma (SCC) of the larynx and hypopharynx are imaged with the system. The proposed nuclei segmentation method based on Principle Component Analysis (PCA) can extract most nuclei in the hyperspectral image without extracting other sub-cellular components. Both spectra-based support vector machine (SVM) and patchbased convolutional neural network (CNN) are used for nuclei classification. CNNs were trained with both hyperspectral images and pseudo RGB images of extracted nuclei, in order to evaluate the usefulness of extra information provided by hyperspectral imaging. The average accuracy of spectra-based SVM classification is 68%. The average AUC and average accuracy of the HSI patch-based CNN classification is 93.7 and 82.4%, respectively. The hyperspectral microscopic imaging and classification methods provide an automatic tool to aid pathologists in detecting SCC on histologic slides.

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 113200W (16 March 2020); doi: 10.1117/12.2549369
Show Author Affiliations
Ling Ma, The Univ. of Texas at Dallas (United States)
Martin Halicek, The Univ. of Texas at Dallas (United States)
Ximing Zhou, The Univ. of Texas at Dallas (United States)
James Dormer, The Univ. of Texas at Dallas (United States)
Baowei Fei, The Univ. of Texas at Dallas (United States)
Univ. of Texas Southwestern Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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