1. Machine-learning based segmentation of the optic nerve head using multi-contrast Jones matrix optical coherence tomography with semi-automatic training dataset generation

    Machine-learning based segmentation of the optic nerve head using multi-contrast Jones matrix optical coherence tomography with semi-automatic training dataset generation

    A pixel-by-pixel tissue classification framework using multiple contrasts obtained by Jones matrix optical coherence tomography (JM-OCT) is demonstrated. The JM-OCT is an extension of OCT that provides OCT, OCT angiography, birefringence tomography, degree-of-polarization uniformity tomography, and attenuation coefficient tomography, simultaneously. The classification framework consists of feature engineering, k -means clustering that generates a training dataset, training of a tissue classifier using the generated training dataset, and tissue classification by the trained classifier. The feature engineering process generates synthetic features from the primary optical contrasts obtained by JM-OCT. The tissue classification is performed in the feature space of the engineered features ...

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