期刊
JOURNAL OF BIOPHOTONICS
卷 11, 期 4, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/jbio.201700072
关键词
image processing; machine-learning; optical coherence tomography
资金
- Ministry of Education and Science of the Russian Federation (RU) [14.B25.31.0015]
- Russian Foundation for Basic Research [15-42-02513_povolzhie, 16-32-60178 mol_a_dk]
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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