Unsupervised segmentation of H and E breast images

Tyna A. Hope; Martin J. Yaffe

doi:10.1117/12.2254047

1 March 2017 Unsupervised segmentation of H and E breast images

Tyna A. Hope, Martin J. Yaffe

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400V (2017) https://doi.org/10.1117/12.2254047
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Heterogeneity of ductal carcinoma in situ (DCIS) continues to be an important topic. Combining biomarker and hematoxylin and eosin (HE) morphology information may provide more insights than either alone. We are working towards a computer-based identification and description system for DCIS. As part of the system we are developing a region of interest finder for further processing, such as identifying DCIS and other HE based measures. The segmentation algorithm is designed to be tolerant of variability in staining and require no user interaction. To achieve stain variation tolerance we use unsupervised learning and iteratively interrogate the image for information. Using simple rules (e.g., “hematoxylin stains nuclei”) and iteratively assessing the resultant objects (small hematoxylin stained objects are lymphocytes), the system builds up a knowledge base so that it is not dependent upon manual annotations. The system starts with image resolution-based assumptions but these are replaced by knowledge gained. The algorithm pipeline is designed to find the simplest items first (segment stains), then interesting subclasses and objects (stroma, lymphocytes), and builds information until it is possible to segment blobs that are normal, DCIS, and the range of benign glands. Once the blobs are found, features can be obtained and DCIS detected. In this work we present the early segmentation results with stains where hematoxylin ranges from blue dominant to red dominant in RGB space.

Citation Download Citation

Tyna A. Hope and Martin J. Yaffe "Unsupervised segmentation of H and E breast images", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400V (1 March 2017); https://doi.org/10.1117/12.2254047

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