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

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

    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 tumour segmentation method was then quantified by comparing with fluorescence imaging of tumors expressing genetically encoded fluorescent protein KillerRed that clearly delineates tumor borders. Since the resultant 3D tumor/normal structural maps are inherently co-registered with OCT derived maps ...

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