The purpose of this study was to develop a generalizable convolutional neural network (CNN) classification technique of optical coherence tomography (OCT) images of breast tissue acquired from multiple OCT systems. We imaged lumpectomy and mastectomy specimens (acquired through the Columbia University Tissue Bank) from 31 patients. In our early results, we classified the images into healthy tissue (adipose and stroma) and diseased, which included ductal carcinoma in situ (DCIS), mucinous carcinoma, and invasive ductal carcinoma (IDC). Our goal is to expand our classification to differentiate the diseased tissue into subclasses of DCIS, IDC, mucinous carcinoma, and benign tissue.
Optical coherence tomography (OCT) is being investigated as an intraoperative margin assessment tool for breast cancer. In this work, we developed a customized deep convolutional neural network (CNN) for classification of breast cancer in OCT images. Images were acquired with a custom ultrahigh-resolution OCT system and a standard resolution system. We classify healthy tissues such as stroma and adipose tissue, as well as diseased tissue including ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Future work involves increasing representation from different kinds of tumors such as mucinous carcinoma, papillary carcinoma, and phyllodes tumors.
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