Volume 11, Issue 4 e201700072
FULL ARTICLE

Pixel classification method in optical coherence tomography for tumor segmentation and its complementary usage with OCT microangiography

Alexander Moiseev

Corresponding Author

Alexander Moiseev

Nano-optics and Highly Sensitive Optical Measurement Department, Institute of Applied Physics Russian Academy of Sciences, Nizhny Novgorod, Russia

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

Correspondence

Alexander Moiseev, Institute of Applied Physics Russian Academy of Sciences, Ulyanova Street 46, 603950 Nizhny Novgorod, Russia. Email: [email protected]

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Ludmila Snopova

Ludmila Snopova

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Sergey Kuznetsov

Sergey Kuznetsov

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Natalia Buyanova

Natalia Buyanova

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Vadim Elagin

Vadim Elagin

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Marina Sirotkina

Marina Sirotkina

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Elena Kiseleva

Elena Kiseleva

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Lev Matveev

Lev Matveev

Nonlinear Geophysical Processes Department, Russian Academy of Sciences, Nizhny Novgorod, Russia

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Vladimir Zaitsev

Vladimir Zaitsev

Nonlinear Geophysical Processes Department, Russian Academy of Sciences, Nizhny Novgorod, Russia

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Felix Feldchtein

Felix Feldchtein

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Elena Zagaynova

Elena Zagaynova

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Valentin Gelikonov

Valentin Gelikonov

Nano-optics and Highly Sensitive Optical Measurement Department, Institute of Applied Physics Russian Academy of Sciences, Nizhny Novgorod, Russia

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Natalia Gladkova

Natalia Gladkova

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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Alex Vitkin

Alex Vitkin

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

Departments of Medical Biophysics and Radiation Oncology, University of Toronto, Ontario, Canada

Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada

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Grigory Gelikonov

Grigory Gelikonov

Nano-optics and Highly Sensitive Optical Measurement Department, Institute of Applied Physics Russian Academy of Sciences, Nizhny Novgorod, Russia

Laboratory of Optical Coherent Tomography, Nizhny Novgorod State Medical Academy, Nizhny Novgorod, Russia

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First published: 29 August 2017
Citations: 27
Funding information Ministry of Education and Science of the Russian Federation (RU), Grant/Award number: 14.B25.31.0015;; Russian Foundation for Basic Research, Grant/Award numbers: 15-42-02513_povolzhie, 16-32-60178 mol_a_dk

Abstract

A novel machine-learning method to distinguish between tumor and normal tissue in optical coherence tomography (OCT) has been developed. Pre-clinical murine ear model implanted with mouse colon carcinoma CT-26 was used. Structural-image-based feature sets were defined for each pixel and machine learning classifiers were trained using “ground truth” OCT images manually segmented by comparison with histology. The accuracy of the OCT tumor segmentation method was then quantified by comparing with fluorescence imaging of tumors expressing genetically encoded fluorescent protein KillerRed that clearly delineates tumor borders. Because the resultant 3D tumor/normal structural maps are inherently co-registered with OCT derived maps of tissue microvasculature, the latter can be color coded as belonging to either tumor or normal tissue. Applications to radiomics-based multimodal OCT analysis are envisioned.

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