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J Biophotonics. 2018 Apr;11(4):e201700072. doi: 10.1002/jbio.201700072. Epub 2017 Dec 18.

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

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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.
Nonlinear Geophysical Processes Department, Russian Academy of Sciences, Nizhny Novgorod, Russia.
Departments of Medical Biophysics and Radiation Oncology, University of Toronto, Ontario, Canada.
Ontario Cancer Institute, University Health Network, Toronto, Ontario, Canada.


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.


image processing; machine-learning; optical coherence tomography


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