TY - CHAP A1 - Marzahl, Christian A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Gerlach, Stefan A1 - Maier, Jennifer A1 - Voigt, Jörn A1 - Hill, Jenny A1 - Klopfleisch, Robert A1 - Maier, Andreas ED - Tolxdorff, Thomas ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph T1 - Is crowd-algorithm collaboration an advanced alternative to crowd-sourcing on cytology slides? T2 - Bildverarbeitung für die Medizin 2020, Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin UR - https://doi.org/10.1007/978-3-658-29267-6_5 Y1 - 2020 UR - https://doi.org/10.1007/978-3-658-29267-6_5 SN - 978-3-658-29266-9 SN - 978-3-658-29267-6 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 26 EP - 31 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Marzahl, Christian A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Maier, Jennifer A1 - Bergler, Christian A1 - Kröger, Christine A1 - Voigt, Jörn A1 - Breininger, Katharina A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control JF - Scientific Reports N2 - In many research areas, scientific progress is accelerated by multidisciplinary access to image data and their interdisciplinary annotation. However, keeping track of these annotations to ensure a high-quality multi-purpose data set is a challenging and labour intensive task. We developed the open-source online platform EXACT (EXpert Algorithm Collaboration Tool) that enables the collaborative interdisciplinary analysis of images from different domains online and offline. EXACT supports multi-gigapixel medical whole slide images as well as image series with thousands of images. The software utilises a flexible plugin system that can be adapted to diverse applications such as counting mitotic figures with a screening mode, finding false annotations on a novel validation view, or using the latest deep learning image analysis technologies. This is combined with a version control system which makes it possible to keep track of changes in the data sets and, for example, to link the results of deep learning experiments to specific data set versions. EXACT is freely available and has already been successfully applied to a broad range of annotation tasks, including highly diverse applications like deep learning supported cytology scoring, interdisciplinary multi-centre whole slide image tumour annotation, and highly specialised whale sound spectroscopy clustering. UR - https://doi.org/10.1038/s41598-021-83827-4 Y1 - 2021 UR - https://doi.org/10.1038/s41598-021-83827-4 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10943 SN - 2045-2322 VL - 11 PB - Springer Nature CY - London ER - TY - JOUR A1 - Goncalves, Miguel A1 - Aubreville, Marc A1 - Müller, Sarina K. A1 - Sievert, Matti A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Bohr, Christopher T1 - Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds JF - ACTA Otorhinolaryngologica Italica N2 - Probe-based confocal laser endomicroscopy (CLE) is an innovative technique for real-time, non-invasive analysis of the surface epithelium. While being successfully used for diagnosis by experts, this method has not yet been established in clinical routine, partly due to the lack of standards and criteria for classifying various lesions. Our aim was to determine the diagnostic value and inter-rater reliability of CLE in detecting malignant lesions of the vocal cords. 58 video sequences were extracted from the probe-based CLE (GastroFlex probe with a Cellvizio® laser system) examinations of 3 patients with squamous cell carcinomas and 4 patients with benign alterations of the vocal folds. Two ENT surgeons, who were blinded to the histological result, were asked to identify the sequences representing a carcinoma. We showed an accuracy, sensitivity, specificity, PPV and NPV of 91.38-96.55%, 100%, 87.8-95.2%, 77.27-89.47% and 100%, respectively, with an inter-rater reliability of k = 0.89 (“almost perfect agreement”). Probe-based CLE is a promising method for diagnosis and assessment of vocal fold lesions in vivo. Our results suggest that, with adequate training, the diagnostic value of this technique can be improved and potentially provide important information during oncological surgery. UR - https://doi.org/10.14639/0392-100X-2121 KW - Confocal laser endomicroscopy KW - Laryngeal cancer KW - Non-invasive histological imaging KW - Vocal folds Y1 - 2019 UR - https://doi.org/10.14639/0392-100X-2121 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-11824 SN - 1827-675X VL - 39 IS - 6 SP - 389 EP - 395 PB - Pacini CY - Pisa 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 - 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 - CHAP A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Jabari, Samir A1 - Marzahl, Christian A1 - Klopfleisch, Robert A1 - Maier, Andreas ED - Tolxdorff, Thomas ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph T1 - Inter-species, inter-tissue domain adaptation for mitotic figure assessment BT - learning new tricks from old dogs T2 - Bildverarbeitung für die Medizin 2020, Algorithmen – Systeme – Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin UR - https://doi.org/10.1007/978-3-658-29267-6_1 Y1 - 2020 UR - https://doi.org/10.1007/978-3-658-29267-6_1 SN - 978-3-658-29266-9 SN - 978-3-658-29267-6 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 1 EP - 7 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Aubreville, Marc A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Jaremenko, Christian A1 - Rodner, Erik A1 - Denzler, Joachim A1 - Bohr, Christopher A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Maier, Andreas T1 - Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning JF - Scientific Reports N2 - Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%). UR - https://doi.org/10.1038/s41598-017-12320-8 Y1 - 2017 UR - https://doi.org/10.1038/s41598-017-12320-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12464 SN - 2045-2322 VL - 7 PB - Springer Nature CY - London ER - TY - CHAP A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - Field of Interest Proposal for Augmented Mitotic Cell Count BT - Comparison of Two Convolutional Networks T2 - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING UR - https://doi.org/10.5220/0007365700300037 KW - Mitotic Figure KW - Cell Segmentation KW - Digital Histopathology KW - Tumor Grading Y1 - 2019 UR - https://doi.org/10.5220/0007365700300037 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-19248 SN - 978-989-758-353-7 SN - 2184-4305 VL - 2 SP - 30 EP - 37 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Aubreville, Marc A1 - Stoeve, Maike A1 - Oetter, Nicolai A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Neumann, Helmut A1 - Bohr, Christopher A1 - Stelzle, Florian A1 - Maier, Andreas T1 - Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images JF - International Journal of Computer Assisted Radiology and Surgery UR - https://doi.org/10.1007/s11548-018-1836-1 KW - Deep convolutional neural networks KW - Confocal laser endomicroscopy KW - Motion artifact detection Y1 - 2019 UR - https://doi.org/10.1007/s11548-018-1836-1 SN - 1861-6429 VL - 14 IS - 1 SP - 31 EP - 42 PB - Springer CY - Berlin ER - TY - JOUR A1 - Bertram, Christof A1 - Aubreville, Marc A1 - Donovan, Taryn A1 - Bartel, Alexander A1 - Wilm, Frauke A1 - Marzahl, Christian 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 - Haverkamp, Ann-Kathrin A1 - Heier, Annabelle A1 - Lehmbecker, Annika A1 - Merz, Sophie A1 - Noland, Erica L. A1 - Plog, Stephanie A1 - Schmidt, Anja A1 - Sebastian, Franziska A1 - Sledge, Dodd G. A1 - Smedley, Rebecca C. A1 - Tecilla, Marco A1 - Thaiwong, Tuddow A1 - Fuchs-Baumgartinger, Andrea A1 - Meuten, Donald J. A1 - Breininger, Katharina A1 - Kiupel, Matti A1 - Maier, Andreas A1 - Klopfleisch, Robert T1 - Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy JF - Veterinary Pathology N2 - The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs. UR - https://doi.org/10.1177/03009858211067478 KW - canine cutaneous mast cell tumors KW - artificial intelligence KW - digital pathology KW - deep learning KW - mitotic figures KW - mitotic count KW - automated image analysis KW - computer assistance Y1 - 2021 UR - https://doi.org/10.1177/03009858211067478 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13141 SN - 1544-2217 VL - 59 IS - 2 SP - 211 EP - 226 PB - Sage CY - London ER - 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 -