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Proceedings Paper

Hotspot detection in pancreatic neuroendocrine images using local depth
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Paper Abstract

There is a recent and increasing trend in the incidence of pancreatic neuroendocrine tumors (PNETs). Ki-67 proliferative index is required for routine pathologic evaluation of PNETs. This index has been found to be a consistent prognostic factor to assess clinical/prognostic outcome of PNETs. Unfortunately, we still lack a standardized method to reliably obtain the Ki-67 proliferative index. As part of a large study to standardize this index, here we present an accurate, easy-to- use, reproducible method to identify tumor nuclei and hotspots within PNETs. We modified the U-Net image segmentation architecture to identify tumor positive and negative nuclei. We also introduced the concept of local depth for identification of hotspots. On an independent test set of 8 whole slide images, the modified U-Net achieved a sensitivity of 96.2% and specificity of 93.3%. The hotspot detection framework resulted in a dice coefficient of 0.81. The method has the potential to not only facilitate the detection of tumor nuclei, but can be adapted to reproduce hotspots by pathologists.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11320, Medical Imaging 2020: Digital Pathology, 1132008 (16 March 2020); doi: 10.1117/12.2550980
Show Author Affiliations
Muhammad Khalid Khan Niazi, Wake Forest School of Medicine (United States)
Katherine Moore, Wake Forest Univ. (United States)
Kenneth S. Berenhaut, Wake Forest Univ. (United States)
Douglas J. Hartman, Univ. of Pittsburgh Medical Ctr. (United States)
Liron Pantanowitz, Univ. of Pittsburgh Medical Ctr. (United States)
Metin N. Gurcan, Wake Forest School of Medicine (United States)


Published in SPIE Proceedings Vol. 11320:
Medical Imaging 2020: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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