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Pixel classification method in optical coherence tomography for tumor segmentation and its complementary usage with OCT microangiography
Author(s) -
Moiseev Alexander,
Snopova Ludmila,
Kuznetsov Sergey,
Buyanova Natalia,
Elagin Vadim,
Sirotkina Marina,
Kiseleva Elena,
Matveev Lev,
Zaitsev Vladimir,
Feldchtein Felix,
Zagaynova Elena,
Gelikonov Valentin,
Gladkova Natalia,
Vitkin Alex,
Gelikonov Grigory
Publication year - 2018
Publication title -
journal of biophotonics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 66
eISSN - 1864-0648
pISSN - 1864-063X
DOI - 10.1002/jbio.201700072
Subject(s) - microangiography , optical coherence tomography , artificial intelligence , feature (linguistics) , computer science , segmentation , ground truth , computer vision , biomedical engineering , pattern recognition (psychology) , pathology , medicine , radiology , linguistics , philosophy
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.