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23 March 2016 A generic nuclei detection method for histopathological breast images
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The detection of cell nuclei plays a key role in various histopathological image analysis problems. Considering the high variability of its applications, we propose a novel generic and trainable detection approach. Adaption to specific nuclei detection tasks is done by providing training samples. A trainable deconvolution and classification algorithm is used to generate a probability map indicating the presence of a nucleus. The map is processed by an extended watershed segmentation step to identify the nuclei positions. We have tested our method on data sets with different stains and target nuclear types. We obtained F1-measures between 0.83 and 0.93.
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Henning Kost, André Homeyer, Peter Bult, Maschenka C. A. Balkenhol, Jeroen A. W. M. van der Laak, and Horst K. Hahn "A generic nuclei detection method for histopathological breast images", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97911E (23 March 2016);

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