TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Gurtner, Corinne A1 - Bartel, Alexander A1 - Corner, Sarah M. A1 - Dettwiler, Martina A1 - Kershaw, Olivia A1 - Noland, Erica L. A1 - Schmidt, Anja A1 - Sledge, Dodd G. A1 - Smedley, Rebecca C. A1 - Thaiwong, Tuddow A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent JF - Veterinary Pathology UR - https://doi.org/10.1177/0300985819890686 KW - area selection KW - high-power field KW - mitotic activity KW - mitotic figure distribution KW - tumor grading KW - tumor periphery Y1 - 2020 UR - https://doi.org/10.1177/0300985819890686 SN - 1544-2217 VL - 57 IS - 2 SP - 214 EP - 226 PB - Sage CY - London ER - TY - JOUR A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Marzahl, Christian A1 - Gurtner, Corinne A1 - Dettwiler, Martina A1 - Schmidt, Anja A1 - Bartenschlager, Florian A1 - Merz, Sophie A1 - Fragoso-Garcia, Marco A1 - Kershaw, Olivia A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region JF - Scientific reports N2 - Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count. UR - https://doi.org/10.1038/s41598-020-73246-2 Y1 - 2020 UR - https://doi.org/10.1038/s41598-020-73246-2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11794 SN - 2045-2322 VL - 10 PB - Springer Nature CY - London ER - TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Gurtner, Corinne A1 - Bartel, Alexander A1 - Corner, Sarah M. A1 - Dettwiler, Martina A1 - Kershaw, Olivia A1 - Noland, Erica L. A1 - Schmidt, Anja A1 - Sledge, Dodd G. A1 - Smedley, Rebecca C. A1 - Thaiwong, Tuddow A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - Mitotic count in canine cutaneous mast cell tumours BT - not accurate but reproducible JF - Journal of Comparative Pathology UR - https://doi.org/10.1016/j.jcpa.2019.10.015 Y1 - 2020 UR - https://doi.org/10.1016/j.jcpa.2019.10.015 SN - 1532-3129 VL - 2020 IS - 174 SP - 143 PB - Elsevier CY - London ER -