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Authors: Marc Aubreville 1 ; Christof Bertram 2 ; Robert Klopfleisch 2 and Andreas Maier 1

Affiliations: 1 Pattern Recognition Lab, Computer Sciences, Friedrich-Alexander-Universität Erlangen-Nürnberg and Germany ; 2 Institute of Veterinary Pathology, Freie Universität Berlin and Germany

ISBN: 978-989-758-353-7

Keyword(s): Mitotic Figure, Cell Segmentation, Digital Histopathology, Tumor Grading.

Related Ontology Subjects/Areas/Topics: Bioimaging ; Biomedical Engineering ; Conventional Microscopy ; Histology and Tissue Imaging ; Medical Imaging and Diagnosis

Abstract: Most tumor grading systems for human as for veterinary histopathology are based upon the absolute count of mitotic figures in a certain reference area of a histology slide. Since time for prognostication is limited in a diagnostic setting, the pathologist will oftentimes almost arbitrarily choose a certain field of interest assumed to have the highest mitotic activity. However, as mitotic figures are commonly very sparse on the slide and often have a patchy distribution, this poses a sampling problem which is known to be able to influence the tumor prognostication. On the other hand, automatic detection of mitotic figures can’t yet be considered reliable enough for clinical application. In order to aid the work of the human expert and at the same time reduce variance in tumor grading, it is beneficial to assess the whole slide image (WSI) for the highest mitotic activity and use this as a reference region for human counting. For this task, we compare two methods for region of interest proposal, both based on convolutional neural networks (CNN). For both approaches, the CNN performs a segmentation of the WSI to assess mitotic activity. The first method performs a segmentation of mitotic cells at the original image resolution, while the second approach performs a segmentation operation at a significantly reduced resolution, cutting down on processing complexity. We evaluate the approach using a dataset of 32 completely annotated whole slide images of canine mast cell tumors, where 22 were used for training of the network and 10 for test. Our results indicate that, while the overall correlation to the ground truth mitotic activity is considerably higher (0.936 vs. 0.829) for the approach based upon the fine resolution network, the field of interest choices are only marginally better. Both approaches propose fields of interest that contain a mitotic count in the upper quartile of respective slides. (More)

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Paper citation in several formats:
Aubreville, M.; Bertram, C.; Klopfleisch, R. and Maier, A. (2019). Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING, ISBN 978-989-758-353-7, pages 30-37. DOI: 10.5220/0007365700300037

@conference{bioimaging19,
author={Marc Aubreville. and Christof A. Bertram. and Robert Klopfleisch. and Andreas Maier.},
title={Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING,},
year={2019},
pages={30-37},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007365700300037},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2 BIOIMAGING: BIOIMAGING,
TI - Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks
SN - 978-989-758-353-7
AU - Aubreville, M.
AU - Bertram, C.
AU - Klopfleisch, R.
AU - Maier, A.
PY - 2019
SP - 30
EP - 37
DO - 10.5220/0007365700300037

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