Slide-specific models for segmentation of differently stained digital histopathology whole slide images

Nicolas Brieu; Olivier Pauly; Johannes Zimmermann; Gerd Binnig; Günter Schmidt

doi:10.1117/12.2208620

21 March 2016 Slide-specific models for segmentation of differently stained digital histopathology whole slide images

Nicolas Brieu, Olivier Pauly, Johannes Zimmermann, Gerd Binnig, Günter Schmidt

Proceedings Volume 9784, Medical Imaging 2016: Image Processing; 978410 (2016) https://doi.org/10.1117/12.2208620
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The automatic analysis of whole slide images (WSIs) of stained histopathology tissue sections plays a crucial role in the discovery of predictive biomarkers in the field on immuno-oncology by enabling the quantification of the phenotypic information contained in the tissue sections. The automatic detection of cells and nuclei, while being one of the major steps of such analysis, remains a difficult problem because of the low visual differentiation of high pleomorphic and densely cluttered objects and of the diversity of tissue appearance between slides. The key idea of this work is to take advantage of well-differentiated objects in each slide to learn about the appearance of the tissue and in particular about the appearance of low-differentiated objects. We detect well-differentiated objects on a automatically selected set of representative regions, learn slide-specific visual context models, and finally use the resulting posterior maps to perform the final detection steps on the whole slide. The accuracy of the method is demonstrated against manual annotations on a set of differently stained images.

Citation Download Citation

Nicolas Brieu, Olivier Pauly, Johannes Zimmermann, Gerd Binnig, and Günter Schmidt "Slide-specific models for segmentation of differently stained digital histopathology whole slide images", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 978410 (21 March 2016); https://doi.org/10.1117/12.2208620

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