Hotspot detection in pancreatic neuroendocrine tumors: density approximation by α-shape maps

M. Khalid Khan Niazi; Douglas J. Hartman; Liron Pantanowitz; Metin N. Gurcan

doi:10.1117/12.2217687

23 March 2016 Hotspot detection in pancreatic neuroendocrine tumors: density approximation by α-shape maps

M. Khalid Khan Niazi, Douglas J. Hartman, Liron Pantanowitz, Metin N. Gurcan

Author Affiliations +

Proceedings Volume 9791, Medical Imaging 2016: Digital Pathology; 97910B (2016) https://doi.org/10.1117/12.2217687
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

The grading of neuroendocrine tumors of the digestive system is dependent on accurate and reproducible assessment of the proliferation with the tumor, either by counting mitotic figures or counting Ki-67 positive nuclei. At the moment, most pathologists manually identify the hotspots, a practice which is tedious and irreproducible. To better help pathologists, we present an automatic method to detect all potential hotspots in neuroendocrine tumors of the digestive system. The method starts by segmenting Ki-67 positive nuclei by entropy based thresholding, followed by detection of centroids for all Ki-67 positive nuclei. Based on geodesic distance, approximated by the nuclei centroids, we compute two maps: an amoeba map and a weighted amoeba map. These maps are later combined to generate the heat map, the segmentation of which results in the hotspots. The method was trained on three and tested on nine whole slide images of neuroendocrine tumors. When evaluated by two expert pathologists, the method reached an accuracy of 92.6%. The current method does not discriminate between tumor, stromal and inflammatory nuclei. The results show that α-shape maps may represent how hotspots are perceived.

Citation Download Citation

M. Khalid Khan Niazi, Douglas J. Hartman, Liron Pantanowitz, and Metin N. Gurcan "Hotspot detection in pancreatic neuroendocrine tumors: density approximation by α-shape maps", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910B (23 March 2016); https://doi.org/10.1117/12.2217687

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