Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks

Conference contribution
(Original article)


Publication Details

Author(s): Aubreville M, Bertram CA, Klopfleisch R, Maier A
Editor(s): SciTePress
Publisher: SciTePress
Publication year: 2019
Conference Proceedings Title: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING
Pages range: 30-37
ISBN: 978-989-758-353-7
Language: English


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.


FAU Authors / FAU Editors

Aubreville, Marc
Lehrstuhl für Informatik 5 (Mustererkennung)
Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)


External institutions with authors

Freie Universität Berlin


How to cite

APA:
Aubreville, M., Bertram, C.A., Klopfleisch, R., & Maier, A. (2019). Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks. In SciTePress (Eds.), Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: BIOIMAGING (pp. 30-37). Prague, CZ: SciTePress.

MLA:
Aubreville, Marc, et al. "Field of Interest Proposal for Augmented Mitotic Cell Count: Comparison of Two Convolutional Networks." Proceedings of the BIOSTEC/BIOIMAGING 2019, Prague Ed. SciTePress, SciTePress, 2019. 30-37.

BibTeX: 

Last updated on 2019-15-03 at 15:53