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Erschienen in:
2017 | Supplement | Buchkapitel
Histological Detection of High-Risk Benign Breast Lesions from Whole Slide Images
verfasst von : Akif Burak Tosun, Luong Nguyen, Nathan Ong, Olga Navolotskaia, Gloria Carter, Jeffrey L. Fine, D. Lansing Taylor, S. Chakra Chennubhotla
Erschienen in: Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017
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Abstract
Accurate diagnosis of high-risk benign breast lesions is crucial in patient management since they are associated with an increased risk of invasive breast cancer development. Since it is not yet possible to identify the occult cancer patients without surgery, this limitation leads to retrospectively unnecessary surgeries. In this paper, we present a computational pathology pipeline for histological diagnosis of high-risk benign breast lesions from whole slide images (WSIs). Our pipeline includes WSI stain color normalization, ductal regions of interest (ROIs) segmentation, and cytological and architectural feature extraction to classify ductal ROIs into triaged high-risk benign lesions. We curated 93 WSIs of breast tissues containing high-risk benign lesions based on pathology reports and collected ground truth annotations from three different pathologists for the ductal ROIs segmented by our pipeline. Our method has comparable performance to a pool of expert pathologists.