Abstract
Optical coherence tomography (OCT) yields microscopic volumetric images representing tissue structures based on the contrast provided by elastic light scattering. Multipatient studies using OCT for detection of tissue abnormalities can lead to large datasets making quantitative and unbiased assessment of classification algorithms performance difficult without the availability of automated analytical schemes. We present a mathematical descriptor reducing the dimensionality of a classifier's input data, while preserving essential volumetric features from reconstructed three-dimensional optical volumes. This descriptor is used as the input of classification algorithms allowing a detailed exploration of the features space leading to optimal and reliable classification models based on support vector machine techniques. Using imaging dataset of paraffin-embedded tissue samples from 38 ovarian cancer patients, we report accuracies for cancer detection [Formula: see text] for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating [Formula: see text] accuracy for the detection of high-grade serous, endometroid, and clear cells cancers. The classification approach reduces the computational complexity and needed resources to achieve highly accurate classification, making it possible to contemplate other applications, including intraoperative surgical guidance, as well as other depth sectioning techniques for fresh tissue imaging.