TY - JOUR A1 - Bertram, Christof A1 - Marzahl, Christian A1 - Bartel, Alexander A1 - Stayt, Jason A1 - Bonsembiante, Federico A1 - Beeler-Marfisi, Janet A1 - Barton, Ann K. A1 - Brocca, Ginevra A1 - Gelain, Maria Elena A1 - Gläsel, Agnes C. A1 - du Preez, Kelly A1 - Weiler, Kristina A1 - Weissenbacher-Lang, Christiane A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Hill, Jenny T1 - Cytologic scoring of equine exercise-induced pulmonary hemorrhage: Performance of human experts and a deep learning-based algorithm JF - Veterinary Pathology N2 - Exercise-induced pulmonary hemorrhage (EIPH) is a relevant respiratory disease in sport horses, which can be diagnosed by examination of bronchoalveolar lavage fluid (BALF) cells using the total hemosiderin score (THS). The aim of this study was to evaluate the diagnostic accuracy and reproducibility of annotators and to validate a deep learning-based algorithm for the THS. Digitized cytological specimens stained for iron were prepared from 52 equine BALF samples. Ten annotators produced a THS for each slide according to published methods. The reference methods for comparing annotator’s and algorithmic performance included a ground truth dataset, the mean annotators’ THSs, and chemical iron measurements. Results of the study showed that annotators had marked interobserver variability of the THS, which was mostly due to a systematic error between annotators in grading the intracytoplasmatic hemosiderin content of individual macrophages. Regarding overall measurement error between the annotators, 87.7% of the variance could be reduced by using standardized grades based on the ground truth. The algorithm was highly consistent with the ground truth in assigning hemosiderin grades. Compared with the ground truth THS, annotators had an accuracy of diagnosing EIPH (THS of < or ≥ 75) of 75.7%, whereas, the algorithm had an accuracy of 92.3% with no relevant differences in correlation with chemical iron measurements. The results show that deep learning-based algorithms are useful for improving reproducibility and routine applicability of the THS. For THS by experts, a diagnostic uncertainty interval of 40 to 110 is proposed. THSs within this interval have insufficient reproducibility regarding the EIPH diagnosis. UR - https://doi.org/10.1177/03009858221137582 KW - artificial intelligence KW - automated image analysis KW - bronchoalveolar lavage fluid KW - computational pathology KW - digital pathology KW - equine KW - pulmonary hemorrhage KW - respiratory disease KW - total hemosiderin score Y1 - 2022 UR - https://doi.org/10.1177/03009858221137582 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-35196 SN - 1544-2217 VL - 60 IS - 1 SP - 75 EP - 85 PB - Sage CY - London ER - TY - JOUR A1 - Sievert, Matti A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Stelzle, Florian A1 - Maier, Andreas A1 - Mantsopoulos, Konstantinos A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Konfokale Laser-Endomikroskopie des Kopf-Hals-Plattenepithelkarzinoms: eine systematische Übersicht JF - Laryngo-Rhino-Otologie T2 - Confocal laser endomicroscopy of head and neck squamous cell carcinoma: a systematic review UR - https://doi.org/10.1055/a-1339-1635 KW - konfokale Laser-Endomikroskopie KW - CLE KW - Kopf-Hals-Malignom KW - Larynxkarzinom KW - Bildanalyse KW - confocal laser endomicroscopy KW - head and neck malignancy KW - laryngeal carcinoma KW - image analysis Y1 - 2021 UR - https://doi.org/10.1055/a-1339-1635 SN - 0935-8943 VL - 100 IS - 11 SP - 875 EP - 881 PB - Thieme CY - Stuttgart ER - TY - JOUR A1 - Sievert, Matti A1 - Stelzle, Florian A1 - Aubreville, Marc A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Mantsopoulos, Konstantinos A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. Methods We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and κ-value of 0.60. Conclusion CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin. UR - https://doi.org/10.1007/s00405-021-06659-y KW - Confocal laser endomicroscopy KW - Oropharyngeal squamous cell carcinoma KW - Safe surgical margins KW - Head and neck malignancies Y1 - 2021 UR - https://doi.org/10.1007/s00405-021-06659-y UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10914 SN - 1434-4726 SN - 0937-4477 VL - 278 IS - 11 SP - 4433 EP - 4439 PB - Springer Nature CY - Cham ER - TY - CHAP A1 - Wilm, Frauke A1 - Bertram, Christof A1 - Marzahl, Christian A1 - Bartel, Alexander A1 - Donovan, Taryn A1 - Assenmacher, Charles-Antoine A1 - Becker, Kathrin A1 - Bennett, Mark A1 - Corner, Sarah M. A1 - Cossic, Brieuc A1 - Denk, Daniela A1 - Dettwiler, Martina A1 - Garcia Gonzalez, Beatriz A1 - Gurtner, Corinne A1 - Heier, Annabelle A1 - Lehmbecker, Annika A1 - Merz, Sophie A1 - Plog, Stephanie A1 - Schmidt, Anja A1 - Sebastian, Franziska A1 - Smedley, Rebecca C. A1 - Tecilla, Marco A1 - Thaiwong, Tuddow A1 - Breininger, Katharina A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Influence of inter-annotator variability on automatic mitotic figure assessment T2 - Bildverarbeitung für die Medizin 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_56 Y1 - 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_56 SN - 978-3-658-33198-6 SP - 241 EP - 246 PB - Springer CY - Wiesbaden ER - TY - CHAP A1 - Bertram, Christof A1 - Donovan, Taryn A1 - Tecilla, Marco A1 - Bartenschlager, Florian A1 - Fragoso-Garcia, Marco A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Breininger, Katharina A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Dataset on bi- and multi-nucleated tumor cells in canine cutaneous mast cell tumors T2 - Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7–9, 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_33 Y1 - 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_33 SN - 978-3-658-33197-9 SN - 978-3-658-33198-6 SN - 1431-472X SP - 134 EP - 139 PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Wilm, Frauke A1 - Voigt, Jörn A1 - Barton, Ann K. A1 - Klopfleisch, Robert A1 - Breininger, Katharina A1 - Maier, Andreas A1 - Aubreville, Marc T1 - Cell detection for asthma on partially annotated whole slide images BT - learning to be EXACT T2 - Bildverarbeitung für die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7–9, 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_36 Y1 - 2021 UR - https://doi.org/10.1007/978-3-658-33198-6_36 SN - 978-3-658-33197-9 SN - 978-3-658-33198-6 SN - 1431-472X SP - 147 EP - 152 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Oetter, Nicolai A1 - Pröll, Jonas A1 - Sievert, Matti A1 - Goncalves, Miguel A1 - Rohde, Maximilian A1 - Nobis, Christopher-Philipp A1 - Knipfer, Christian A1 - Aubreville, Marc A1 - Pan, Zhaoya A1 - Breininger, Katharina A1 - Maier, Andreas A1 - Kesting, Marco A1 - Stelzle, Florian T1 - Oral mucosa - an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study JF - Clinical Oral Investigations N2 - Objectives Confocal laser endomicroscopy (CLE) is an optical method that enables microscopic visualization of oral mucosa. Previous studies have shown that it is possible to differentiate between physiological and malignant oral mucosa. However, differences in mucosal architecture were not taken into account. The objective was to map the different oral mucosal morphologies and to establish a “CLE map” of physiological mucosa as baseline for further application of this powerful technology. Materials and methods The CLE database consisted of 27 patients. The following spots were examined: (1) upper lip (intraoral) (2) alveolar ridge (3) lateral tongue (4) floor of the mouth (5) hard palate (6) intercalary line. All sequences were examined by two CLE experts for morphological differences and video quality. Results Analysis revealed clear differences in image quality and possibility of depicting tissue morphologies between the various localizations of oral mucosa: imaging of the alveolar ridge and hard palate showed visually most discriminative tissue morphology. Labial mucosa was also visualized well using CLE. Here, typical morphological features such as uniform cells with regular intercellular gaps and vessels could be clearly depicted. Image generation and evaluation was particularly difficult in the area of the buccal mucosa, the lateral tongue and the floor of the mouth. Conclusion A physiological “CLE map” for the entire oral cavity could be created for the first time. Clinical relevance This will make it possible to take into account the existing physiological morphological features when differentiating between normal mucosa and oral squamous cell carcinoma in future work. UR - https://doi.org/10.1007/s00784-024-05664-9 KW - Confocal laser endomicroscopy KW - Oral cavity KW - Oral carcinoma KW - Optical methods KW - Oral cavity squamous cell carcinoma KW - Head and neck cancer Y1 - 2024 UR - https://doi.org/10.1007/s00784-024-05664-9 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-56000 SN - 1436-3771 VL - 28 IS - 5 PB - Springer CY - Berlin ER - TY - JOUR A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Marzahl, Christian A1 - Qiu, Jingna A1 - Puget, Chloé A1 - Diehl, Laura A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Breininger, Katharina A1 - Aubreville, Marc T1 - Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset JF - Scientific Data N2 - Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application. UR - https://doi.org/10.1038/s41597-022-01692-w Y1 - 2022 UR - https://doi.org/10.1038/s41597-022-01692-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28741 SN - 2052-4463 VL - 9 PB - Springer CY - London ER - TY - JOUR A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Donovan, Taryn A1 - Marzahl, Christian A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research JF - Scientific data N2 - Canine mammary carcinoma (CMC) has been used as a model to investigate the pathogenesis of human breast cancer and the same grading scheme is commonly used to assess tumor malignancy in both. One key component of this grading scheme is the density of mitotic figures (MF). Current publicly available datasets on human breast cancer only provide annotations for small subsets of whole slide images (WSIs). We present a novel dataset of 21 WSIs of CMC completely annotated for MF. For this, a pathologist screened all WSIs for potential MF and structures with a similar appearance. A second expert blindly assigned labels, and for non-matching labels, a third expert assigned the final labels. Additionally, we used machine learning to identify previously undetected MF. Finally, we performed representation learning and two-dimensional projection to further increase the consistency of the annotations. Our dataset consists of 13,907 MF and 36,379 hard negatives. We achieved a mean F1-score of 0.791 on the test set and of up to 0.696 on a human breast cancer dataset. UR - https://doi.org/10.1038/s41597-020-00756-z Y1 - 2020 UR - https://doi.org/10.1038/s41597-020-00756-z UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11765 SN - 2052-4463 VL - 7 PB - Springer CY - London ER - TY - JOUR A1 - Marzahl, Christian A1 - Hill, Jenny A1 - Stayt, Jason A1 - Bienzle, Dorothee A1 - Welker, Lutz A1 - Wilm, Frauke A1 - Voigt, Jörn A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Breininger, Katharina A1 - Bertram, Christof T1 - Inter-species cell detection - datasets on pulmonary hemosiderophages in equine, human and feline specimens JF - Scientific Data N2 - Pulmonary hemorrhage (P-Hem) occurs among multiple species and can have various causes. Cytology of bronchoalveolar lavage fluid (BALF) using a 5-tier scoring system of alveolar macrophages based on their hemosiderin content is considered the most sensitive diagnostic method. We introduce a novel, fully annotated multi-species P-Hem dataset, which consists of 74 cytology whole slide images (WSIs) with equine, feline and human samples. To create this high-quality and high-quantity dataset, we developed an annotation pipeline combining human expertise with deep learning and data visualisation techniques. We applied a deep learning-based object detection approach trained on 17 expertly annotated equine WSIs, to the remaining 39 equine, 12 human and 7 feline WSIs. The resulting annotations were semi-automatically screened for errors on multiple types of specialised annotation maps and finally reviewed by a trained pathologist. Our dataset contains a total of 297,383 hemosiderophages classified into five grades. It is one of the largest publicly available WSIs datasets with respect to the number of annotations, the scanned area and the number of species covered. UR - https://doi.org/10.1038/s41597-022-01389-0 Y1 - 2022 UR - https://doi.org/10.1038/s41597-022-01389-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29319 SN - 2052-4463 VL - 9 PB - Springer CY - London ER - TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Marzahl, Christian A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor JF - Scientific data N2 - We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms. UR - https://doi.org/10.1038/s41597-019-0290-4 Y1 - 2019 UR - https://doi.org/10.1038/s41597-019-0290-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11838 SN - 2052-4463 VL - 6 PB - Springer CY - London 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 - TY - INPR A1 - Bertram, Christof A1 - Veta, Mitko A1 - Marzahl, Christian A1 - Stathonikos, Nikolas A1 - Maier, Andreas A1 - Klopfleisch, Robert A1 - Aubreville, Marc T1 - Are pathologist-defined labels reproducible? Comparison of the TUPAC16 mitotic figure dataset with an alternative set of labels UR - https://doi.org/10.48550/arXiv.2007.05351 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2007.05351 PB - arXiv CY - Ithaca ER -