1. Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography

    Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography

    We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT). A total of 720 PS-OCT images from 72 sites of 41 patients with H&E histology-confirmed diagnoses as the gold standard were employed in this study. The differentiation model is trained by the features extracted from both one standard OCT-based metric (i.e., intensity) and four PS-OCT-based metrics (i.e., phase difference between two channels ( PD ), phase retardation ( PR ), local phase retardation ( LPR ), and degree of polarization uniformity ( DOPU )). Further optimized by forward searching and validated ...

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