TY - JOUR A1 - Marzahl, Christian A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Stayt, Jason A1 - Jasensky, Anne-Katherine A1 - Bartenschlager, Florian A1 - Fragoso-Garcia, Marco A1 - Barton, Ann K. A1 - Elsemann, Svenja A1 - Jabari, Samir A1 - Krauth, Jens A1 - Madhu, Prathmesh A1 - Voigt, Jörn A1 - Hill, Jenny A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Deep Learning-based quantification of pulmonary hemosiderophages in cytology slides JF - Scientific Reports N2 - Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter- and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss’ kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter- and intra-rater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI. UR - https://doi.org/10.1038/s41598-020-65958-2 Y1 - 2020 UR - https://doi.org/10.1038/s41598-020-65958-2 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11783 SN - 2045-2322 VL - 10 PB - Springer Nature CY - London ER - TY - CHAP A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Petrick, Anne A1 - Weiler, Kristina A1 - Gläsel, Agnes C. A1 - Fragoso-Garcia, Marco A1 - Merz, Sophie A1 - Bartenschlager, Florian A1 - Hoppe, Judith A1 - Langenhagen, Alina A1 - Jasensky, Anne-Katherine A1 - Voigt, Jörn A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides T2 - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 UR - https://doi.org/10.1007/978-3-030-59710-8_3 KW - Pathology KW - Microscopy KW - Computer-aided labelling KW - Expert-algorithm collaboration Y1 - 2020 UR - https://doi.org/10.1007/978-3-030-59710-8_3 SN - 978-3-030-59710-8 SN - 1611-3349 SP - 24 EP - 32 PB - Springer CY - Cham ER - TY - INPR A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Petrick, Anne A1 - Weiler, Kristina A1 - Gläsel, Agnes C. A1 - Fragoso-Garcia, Marco A1 - Merz, Sophie A1 - Bartenschlager, Florian A1 - Hoppe, Judith A1 - Langenhagen, Alina A1 - Jasensky, Anne-Katherine A1 - Voigt, Jörn A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides UR - https://doi.org/10.48550/arXiv.2004.05838 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2004.05838 PB - arXiv CY - Ithaca ER -