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 - TY - CHAP A1 - Aubreville, Marc A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Würfl, Tobias A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Bohr, Christopher A1 - Maier, Andreas ED - Wiebe, Sheldon ED - Gamboa, Hugo ED - Fred, Ana ED - Bermúdez i Badia, Sergi T1 - Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images BT - A Cross-Site Robustness Assessment T2 - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies N2 - Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accurac y of 89.45% and an area-under-the-curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. This study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner. UR - https://doi.org/10.5220/0006534700270034 KW - Automatic Carcinoma Detection KW - Confocal Laser Endomicroscopy KW - Deep Convolutional Networks KW - Squamous Cell Carcinoma Y1 - 2018 UR - https://doi.org/10.5220/0006534700270034 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-26277 SN - 978-989-758-278-3 SP - 27 EP - 34 PB - SciTePress CY - Setúbal ER - TY - JOUR A1 - Sievert, Matti A1 - Oetter, Nicolai A1 - Aubreville, Marc A1 - Stelzle, Florian A1 - Maier, Andreas A1 - Eckstein, Markus A1 - Mantsopoulos, Konstantinos A1 - Gostian, Antoniu-Oreste A1 - Müller, Sarina K. A1 - Koch, Michael A1 - Agaimy, Abbas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy BT - a pilot study JF - Auris Nasus Larynx UR - https://doi.org/10.1016/j.anl.2021.01.005 Y1 - 2021 UR - https://doi.org/10.1016/j.anl.2021.01.005 SN - 1879-1476 VL - 48 IS - 4 SP - 764 EP - 769 PB - Elsevier Science CY - Amsterdam ER - TY - CHAP A1 - Marzahl, Christian A1 - Wilm, Frauke A1 - Tharun, Lars A1 - Perner, Sven A1 - Kröger, Christine A1 - Voigt, Jörn A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Robust quad-tree based registration on whole slide images T2 - Proceedings of Machine Learning Research: Proceedings of COMPAY 2021 KW - Registration KW - Microscopy KW - Pathology Y1 - 2021 UR - https://proceedings.mlr.press/v156/marzahl21a.html IS - 156 SP - 181 EP - 190 PB - PMLR CY - [s. l.] ER - TY - CHAP A1 - Ganz, Jonathan A1 - Kirsch, Tobias A1 - Hoffmann, Lucas A1 - Maier, Andreas A1 - Breininger, Katharina A1 - Blümcke, Ingmar A1 - Jabari, Samir A1 - Aubreville, Marc T1 - Automatic and explainable grading of meningiomas from histopathology images T2 - Proceedings of Machine Learning Research: Proceedings of COMPAY 2021 KW - automatic tumor grading KW - meningioma KW - known operator learning KW - deep learning Y1 - 2021 UR - https://proceedings.mlr.press/v156/ganz21a.html VL - 2021 IS - 156 SP - 69 EP - 80 PB - PMLR CY - [s. l.] ER - TY - CHAP A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas ED - Handels, Heinz ED - Deserno, Thomas Martin ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Augmented mitotic cell count using field of interest proposal T2 - Bildverarbeitung für die Medizin 2019, Algorithmen – Systeme – Anwendungen, Proceedings des Workshops vom 17. bis 19. März 2019 in Lübeck UR - https://doi.org/10.1007/978-3-658-25326-4_71 Y1 - 2019 UR - https://doi.org/10.1007/978-3-658-25326-4_71 SN - 978-3-658-25325-7 SN - 978-3-658-25326-4 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 321 EP - 326 PB - Springer Vieweg CY - Wiesbaden 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 - CHAP A1 - Aubreville, Marc A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Würfl, Tobias A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Bohr, Christopher A1 - Maier, Andreas T1 - Transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract T2 - Biomedical Engineering Systems and Technologies UR - https://doi.org/10.1007/978-3-030-29196-9_4 KW - Confocal Laser Endomicroscopy KW - Transfer learning KW - Head and neck squamous cell carcinoma Y1 - 2019 UR - https://doi.org/10.1007/978-3-030-29196-9_4 SN - 978-3-030-29195-2 SN - 978-3-030-29196-9 SN - 1865-0929 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 67 EP - 85 PB - Springer CY - Cham ER - TY - CHAP A1 - Stoeve, Maike A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Knipfer, Christian A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Maier, Andreas ED - Maier, Andreas ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Motion Artifact Detection in Confocal Laser Endomicroscopy Images T2 - Bildverarbeitung für die Medizin 2018: Algorithmen - Systeme - Anwendungen UR - https://doi.org/10.1007/978-3-662-56537-7_85 Y1 - 2018 UR - https://doi.org/10.1007/978-3-662-56537-7_85 SN - 978-3-662-56537-7 SP - 328 EP - 333 PB - Springer Vieweg CY - Berlin 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 - TY - CHAP A1 - Steffes, Lara-Maria A1 - Aubreville, Marc A1 - Sesselmann, Stefan A1 - Krenn, Veit A1 - Maier, Andreas ED - Maier, Andreas ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slices T2 - Bildverarbeitung für die Medizin 2018 UR - https://doi.org/10.1007/978-3-662-56537-7_63 Y1 - 2018 UR - https://doi.org/10.1007/978-3-662-56537-7_63 SN - 978-3-662-56536-0 SP - 228 EP - 233 PB - Springer CY - Berlin ER - TY - INPR A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Bertram, Christof A1 - Stathonikos, Nikolas A1 - Öttl, Mathias A1 - Qiu, Jingna A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology UR - https://doi.org/10.48550/arXiv.2211.16141 KW - Histopathology KW - Domain Shift KW - Representation Learning KW - Barlow Twins Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2211.16141 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Aubreville, Marc A1 - Krappmann, Maximilian A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - A Guided Spatial Transformer Network for Histology Cell Differentiation T2 - VCBM '17: Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine UR - https://doi.org/10.2312/vcbm.20171233 Y1 - 2017 UR - https://doi.org/10.2312/vcbm.20171233 SN - 978-3-03868-036-9 SP - 21 EP - 25 PB - Eurographics Association CY - Goslar ER - TY - CHAP A1 - Mualla, Firas A1 - Aubreville, Marc A1 - Maier, Andreas ED - Maier, Andreas ED - Steidl, Stefan ED - Christlein, Vincent ED - Hornegger, Joachim T1 - Microscopy T2 - Medical Imaging Systems: An Introductory Guide N2 - We perceive the physical world around us using our eyes, but only down to a certain limit. Objects with a diameter smaller than 75 μm cannot be recognized by the naked eye, and due to this reason, they remained undiscovered for the most of human history. UR - https://doi.org/10.1007/978-3-319-96520-8_5 Y1 - 2018 UR - https://doi.org/10.1007/978-3-319-96520-8_5 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-26411 SN - 978-3-319-96520-8 SP - 69 EP - 90 PB - Springer CY - Cham ER - TY - INPR A1 - Stoeve, Maike A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Knipfer, Christian A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Maier, Andreas T1 - Motion Artifact Detection in Confocal Laser Endomicroscopy Images N2 - Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing non-invasive examination of the mucosa on a (sub)- cellular level, has proven to be a valuable diagnostic tool in gastroenterology and shows promising results in various anatomical regions including the oral cavity. Recently, the feasibility of automatic carcinoma detection for CLE images of sufficient quality was shown. However, in real world data sets a high amount of CLE images is corrupted by artifacts. Amongst the most prevalent artifact types are motion-induced image deteriorations. In the scope of this work, algorithmic approaches for the automatic detection of motion artifact-tainted image regions were developed. Hence, this work provides an important step towards clinical applicability of automatic carcinoma detection. Both, conventional machine learning and novel, deep learning-based approaches were assessed. The deep learning-based approach outperforms the conventional approaches, attaining an AUC of 0.90. UR - https://doi.org/10.48550/arXiv.1711.01117 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1711.01117 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12588 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - SlideRunner BT - a tool for massive cell annotations in whole slide images T2 - Bildverarbeitung für die Medizin 2018: Algorithmen - Systeme - Anwendungen N2 - Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do not match the prerequisites for fast annotations. Especially in the field of mitosis detection, it is assumed that detection accuracy could significantly benefit from larger annotation databases that are currently however very troublesome to produce. Further, multiple independent (blind) expert labels are a big asset for such databases, yet there is currently no tool for this kind of annotation available. To ease this tedious process of expert annotation and grading, we introduce SlideRunner, an open source annotation and visualization tool for digital histopathology, developed in close cooperation with two pathologists. SlideRunner is capable of setting annotations like object centers (for e.g. cells) as well as object boundaries (e.g. for tumor outlines). It provides single-click annotations as well as a blind mode for multi-annotations, where the expert is directly shown the microscopy image containing the cells that he has not yet rated. UR - https://doi.org/10.1007/978-3-662-56537-7_81 Y1 - 2018 UR - https://doi.org/10.1007/978-3-662-56537-7_81 SN - 978-3-662-56537-7 SP - 309 EP - 314 PB - Springer Vieweg CY - Berlin 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 - CHAP A1 - Schröter, H. A1 - Rosenkranz, Tobias A1 - Escalante-B, A. N. A1 - Aubreville, Marc A1 - Maier, Andreas T1 - CLCNET: deep learning-based noise reduction for hearing aids using complex linear coding T2 - ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) UR - https://doi.org/10.1109/ICASSP40776.2020.9053563 KW - noise reduction KW - speech enhancement KW - LPC KW - hearing aid signal processing KW - deep learning Y1 - 2020 UR - https://doi.org/10.1109/ICASSP40776.2020.9053563 SN - 978-1-5090-6631-5 SP - 6949 EP - 6953 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Marzahl, Christian A1 - Aubreville, Marc A1 - Voigt, Jörn A1 - Maier, Andreas T1 - Classification of leukemic b-lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques T2 - ISBI 2019 C-NMC challenge: classification in cancer cell imaging UR - https://doi.org/10.1007/978-981-15-0798-4_2 Y1 - 2019 UR - https://doi.org/10.1007/978-981-15-0798-4_2 SN - 978-981-15-0797-7 SN - 978-981-15-0798-4 SN - 978-981-15-0800-4 SN - 2195-271X SN - 2195-2728 SP - 13 EP - 22 PB - Springer CY - Singapore ER - TY - CHAP 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 T2 - Interpretable and Annotation-Efficient Learning for Medical Image Computing UR - https://doi.org/10.1007/978-3-030-61166-8_22 KW - Breast cancer KW - Mitotic figures KW - Computer-aided annotation KW - Deep learning Y1 - 2020 UR - https://doi.org/10.1007/978-3-030-61166-8_22 SN - 978-3-030-61166-8 SN - 1611-3349 SP - 204 EP - 213 PB - Springer CY - Cham ER - TY - CHAP A1 - Aubreville, Marc A1 - Ehrensperger, Kai A1 - Maier, Andreas A1 - Rosenkranz, Tobias A1 - Graf, Benjamin A1 - Puder, Henning T1 - Deep Denoising for Hearing Aid Applications T2 - IWAENC Proceedings UR - https://doi.org/10.1109/IWAENC.2018.8521369 KW - noise reduction KW - hearing aid signal processing KW - deep neural networks Y1 - 2018 UR - https://doi.org/10.1109/IWAENC.2018.8521369 SN - 978-1-5386-8151-0 SP - 361 EP - 365 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Krappmann, Maximilian A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Bertram, Christof A1 - Klopfleisch, Robert ED - Maier, Andreas ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Classification of Mitotic Cells BT - Potentials Beyond the Limits of Small Data Sets T2 - Bildverarbeitung für die Medizin 2018 UR - https://doi.org/10.1007/978-3-662-56537-7_66 Y1 - 2018 UR - https://doi.org/10.1007/978-3-662-56537-7_66 SN - 978-3-662-56536-0 SP - 245 EP - 250 PB - Springer CY - Berlin ER - TY - INPR A1 - Aubreville, Marc A1 - Krappmann, Maximilian A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas T1 - A Guided Spatial Transformer Network for Histology Cell Differentiation UR - https://doi.org/10.48550/arXiv.1707.08525 Y1 - 2017 UR - https://doi.org/10.48550/arXiv.1707.08525 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Sievert, Matti A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Rupp, Robin A1 - Aubreville, Marc A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Systematic interpretation of confocal laser endomicroscopy: larynx and pharynx confocal imaging score JF - Acta otorhinolaryngologica italica N2 - Objective. Development and validation of a confocal laser endomicroscopy (CLE) classification score for the larynx and pharynx. Methods. Thirteen patients (154 video sequences, 9240 images) with laryngeal or pharyngeal SCC were included in this prospective study between October 2020 and February 2021. Each CLE sequence was correlated with the gold standard of histopathological examination. Based on a dataset of 94 video sequences (5640 images), a scoring system was developed. In the remaining 60 sequences (3600 images), the score was validated by four CLE experts and four head and neck surgeons who were not familiar with CLE. Results. Tissue homogeneity, cell size, borders and clusters, capillary loops and the nucleus/ cytoplasm ratio were defined as the scoring criteria. Using this score, the CLE experts obtained an accuracy, sensitivity, and specificity of 90.8%, 95.1%, and 86.4%, respectively, and the CLE non-experts of 86.2%, 86.4%, and 86.1%. Interobserver agreement Fleiss’ kappa was 0.8 and 0.6, respectively. Conclusions. CLE can be reliably evaluated based on defined and reproducible imaging features, which demonstrate a high diagnostic value. CLE can be easily integrated into the intraoperative setting and generate real-time, in-vivo microscopic images to demarcate malignant changes. UR - https://doi.org/10.14639/0392-100X-N1643 KW - confocal laser endomicroscopy KW - head and neck cancer KW - classification system KW - non-invasive histological imaging KW - larynx KW - pharynx Y1 - 2022 UR - https://doi.org/10.14639/0392-100X-N1643 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-29175 SN - 1827-675X VL - 42 IS - 1 SP - 26 EP - 33 PB - Pacini CY - Pisa ER - TY - CHAP A1 - Wilm, Frauke A1 - Fragoso-Garcia, Marco A1 - Bertram, Christof A1 - Stathonikos, Nikolas A1 - Öttl, Mathias A1 - Qiu, Jingna A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Mind the Gap: Scanner-Induced Domain Shifts Pose Challenges for Representation Learning in Histopathology T2 - 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI53787.2023.10230458 KW - Histopathology KW - Domain Shift KW - Representation Learning KW - Barlow Twins Y1 - 2023 UR - https://doi.org/10.1109/ISBI53787.2023.10230458 SN - 978-1-6654-7358-3 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Wilm, Frauke A1 - Ihling, Christian A1 - Méhes, Gábor A1 - Terracciano, Luigi A1 - Puget, Chloé A1 - Klopfleisch, Robert A1 - Schüffler, Peter A1 - Aubreville, Marc A1 - Maier, Andreas A1 - Mrowiec, Thomas A1 - Breininger, Katharina T1 - Pan-tumor T-lymphocyte detection using deep neural networks: Recommendations for transfer learning in immunohistochemistry JF - Journal of Pathology Informatics N2 - The success of immuno-oncology treatments promises long-term cancer remission for an increasing number of patients. The response to checkpoint inhibitor drugs has shown a correlation with the presence of immune cells in the tumor and tumor microenvironment. An in-depth understanding of the spatial localization of immune cells is therefore critical for understanding the tumor’s immune landscape and predicting drug response. Computer-aided systems are well suited for efficiently quantifying immune cells in their spatial context. Conventional image analysis approaches are often based on color features and therefore require a high level of manual interaction. More robust image analysis methods based on deep learning are expected to decrease this reliance on human interaction and improve the reproducibility of immune cell scoring. However, these methods require sufficient training data and previous work has reported low robustness of these algorithms when they are tested on out-of-distribution data from different pathology labs or samples from different organs. In this work, we used a new image analysis pipeline to explicitly evaluate the robustness of marker-labeled lymphocyte quantification algorithms depending on the number of training samples before and after being transferred to a new tumor indication. For these experiments, we adapted the RetinaNet architecture for the task of T-lymphocyte detection and employed transfer learning to bridge the domain gap between tumor indications and reduce the annotation costs for unseen domains. On our test set, we achieved human-level performance for almost all tumor indications with an average precision of 0.74 in-domain and 0.72–0.74 cross-domain. From our results, we derive recommendations for model development regarding annotation extent, training sample selection, and label extraction for the development of robust algorithms for immune cell scoring. By extending the task of marker-labeled lymphocyte quantification to a multi-class detection task, the pre-requisite for subsequent analyses, e.g., distinguishing lymphocytes in the tumor stroma from tumor-infiltrating lymphocytes, is met. UR - https://doi.org/10.1016/j.jpi.2023.100301 KW - Tumor-infiltrating lymphocytes KW - Immuno-oncology KW - Immunohistochemistry KW - Deep learning KW - Transfer learning KW - Domain adaptation Y1 - 2023 UR - https://doi.org/10.1016/j.jpi.2023.100301 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40458 SN - 2153-3539 VL - 2023 IS - 14 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Fragoso-Garcia, Marco A1 - Wilm, Frauke A1 - Bertram, Christof A1 - Merz, Sophie A1 - Schmidt, Anja A1 - Donovan, Taryn A1 - Fuchs-Baumgartinger, Andrea A1 - Bartel, Alexander A1 - Marzahl, Christian A1 - Diehl, Laura A1 - Puget, Chloe A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina A1 - Klopfleisch, Robert T1 - Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images JF - Veterinary Pathology N2 - Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation ( n = 35 WSIs), and test sets ( n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types. UR - https://doi.org/10.1177/03009858231189205 KW - computer-aided diagnosis KW - computational pathology KW - digital pathology KW - dog KW - machine learning KW - skin KW - veterinary oncology Y1 - 2023 UR - https://doi.org/10.1177/03009858231189205 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38321 SN - 0300-9858 VL - 60 IS - 6 SP - 865 EP - 875 PB - Sage CY - London ER - TY - JOUR A1 - Sievert, Matti A1 - Aubreville, Marc A1 - Gostian, Antoniu-Oreste A1 - Mantsopoulos, Konstantinos A1 - Koch, Michael A1 - Müller, Sarina K. A1 - Eckstein, Markus A1 - Rupp, Robin A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose Confocal laser endomicroscopy (CLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. This study analyzes differences in tissue homogeneity between healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods We included five SCC patients with planned total laryngectomy in this study between October 2020 and February 2021. We captured CLE scans of the tumor and healthy mucosa. Analysis of image homogeneity to diagnose SCC was performed by measuring the signal intensity in four regions of interest (ROI) in each frame in a total of 60 sequences. Each sequence was assigned to the corresponding histological pattern, derived from hematoxylin and eosin staining. In addition, we recorded the subjective evaluation of seven investigators regarding tissue homogeneity. Results Out of 3600 images, 1620 (45%) correlated with benign mucosa and 1980 (55%) with SCC. ROIs of benign mucosa and SCC had a mean and standard deviation (SD) of signal intensity of, respectively, 232.1 ± 3.34 and 467.3 ± 9.72 (P < 0.001). The mean SD between the four different ROIs was 39.1 ± 1.03 for benign and 101.5 ± 2.6 for SCC frames (P < 0.001). In addition, homogeneity yielded a sensitivity and specificity of 81.8% and 86.2%, respectively, regarding the investigator-dependent analysis. Conclusions SCC shows a significant tissue inhomogeneity in comparison to the healthy epithelium. The results support this feature’s importance in identifying malignant mucosa areas during CLE examination. However, the examiner-dependent evaluation emphasizes that homogeneity is a sub-criterion that must be considered in a broad context. UR - https://doi.org/10.1007/s00405-022-07304-y KW - confocal laser endomicroscopy KW - head and neck cancer KW - classification system KW - non-invasive histological imaging KW - larynx KW - pharynx Y1 - 2022 UR - https://doi.org/10.1007/s00405-022-07304-y UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23180 SN - 1434-4726 SN - 0937-4477 VL - 279 IS - 8 SP - 4147 EP - 4156 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Sievert, Matti A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Rupp, Robin A1 - Eckstein, Markus A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy JF - Brazilian Journal of Otorhinolaryngology N2 - Introduction Confocal laser endomicroscopy is an optical imaging technique that allows in vivo, real-time, microscope-like images of the upper aerodigestive tract’s mucosa. The assessment of morphological tissue characteristics for the correct differentiation between healthy and malignant suspected mucosa requires strict evaluation criteria. Objective This study aims to validate an eight-point score for the correct assessment of malignancy. Methods We performed confocal laser endomicroscopy between March and October 2020 in 13 patients. 197 sequences (11.820 images) originated from the marginal area of pharyngeal and laryngeal carcinomas. Specimens were taken at corresponding locations and analyzed in H&E staining as a standard of reference. A total of six examiners evaluated the sequences based on a scoring system; they were blinded to the histopathological examination. The primary endpoints are sensitivity, specificity, and accuracy. Secondary endpoints are interrater reliability and receiver operator characteristics. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. Confocal laser endomicroscopy of malignant cells demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 83.2%, 81.3%, 85.5%, 86.7%, and 79.7%, respectively, with a κ-value of 0.64, and an area under the curve of 0.86. Conclusion The results confirm that this scoring system is applicable in the laryngeal and pharyngeal mucosa to classify benign and malignant tissue. A scoring system based on defined and reproducible characteristics can help translate this experimental method to broad clinical practice in head and neck diagnosis. UR - https://doi.org/10.1016/j.bjorl.2021.06.002 KW - confocal laser endomicroscopy KW - squamous cell carcinoma KW - head and neck cancer KW - diagnosis Y1 - 2021 UR - https://doi.org/10.1016/j.bjorl.2021.06.002 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-20887 SN - 1808-8694 VL - 2022 IS - 88, S4 SP - 26 EP - 32 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Sievert, Matti A1 - Eckstein, Markus A1 - Mantsopoulos, Konstantinos A1 - Müller, Sarina K. A1 - Stelzle, Florian A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Iro, Heinrich A1 - Goncalves, Miguel T1 - Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma JF - European Archives of Oto-Rhino-Laryngology and Head & Neck N2 - Purpose Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE. Materials and methods We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC. Results In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%; P < .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%; P < .001), and increased capillary leakage (40.7% vs. 2.5%; P < .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture. Conclusion Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination. UR - https://doi.org/10.1007/s00405-021-06954-8 KW - confocal laser endomicroscopy KW - head and neck squamous cell carcinoma KW - capillary loops KW - malignant vascularization KW - head and neck malignancies Y1 - 2021 UR - https://doi.org/10.1007/s00405-021-06954-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-23196 SN - 1434-4726 SN - 0937-4477 VL - 279 IS - 4 SP - 2029 EP - 2037 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Aubreville, Marc A1 - Stathonikos, Nikolas A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Hoeve, Natalie ter A1 - Ciompi, Francesco A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Donovan, Taryn A1 - Maier, Andreas A1 - Breen, Jack A1 - Ravikumar, Nishant A1 - Chung, Youjin A1 - Park, Jinah A1 - Nateghi, Ramin A1 - Pourakpour, Fattaneh A1 - Fick, Rutger H. J. A1 - Ben Hadj, Saima A1 - Jahanifar, Mostafa A1 - Shepard, Adam A1 - Dexl, Jakob A1 - Wittenberg, Thomas A1 - Kondo, Satoshi A1 - Lafarge, Maxime W. A1 - Kolezer, Viktor H. A1 - Liang, Jingtang A1 - Wang, Yubo A1 - Long, Xi A1 - Liu, Jingxin A1 - Razavi, Salar A1 - Khademi, April A1 - Yang, Sen A1 - Wang, Xiyue A1 - Erber, Ramona A1 - Klang, Andrea A1 - Lipnik, Karoline A1 - Bolfa, Pompei A1 - Dark, Michael A1 - Wasinger, Gabriel A1 - Veta, Mitko A1 - Breininger, Katharina T1 - Mitosis domain generalization in histopathology images — The MIDOG challenge JF - Medical Image Analysis UR - https://doi.org/10.1016/j.media.2022.102699 KW - Domain generalization KW - Histopathology KW - Challenge KW - Deep Learning KW - Mitosis Y1 - 2022 UR - https://doi.org/10.1016/j.media.2022.102699 SN - 1361-8423 SN - 1361-8415 VL - 2023 IS - 84 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Pan, Zhaoya A1 - Breininger, Katharina A1 - Aubreville, Marc A1 - Stelzle, Florian A1 - Oetter, Nicolai A1 - Maier, Andreas A1 - Mantsopoulos, Konstantinos A1 - Iro, Heinrich A1 - Goncalves, Miguel A1 - Sievert, Matti T1 - Defining a baseline identification of artifacts in confocal laser endomicroscopy in head and neck cancer imaging JF - American Journal of Otolaryngology UR - https://doi.org/10.1016/j.amjoto.2022.103779 KW - Confocal laser endomicroscopy KW - Head and neck cancer KW - Classification system KW - Non-invasive histological imaging KW - Larynx KW - Pharynx Y1 - 2022 UR - https://doi.org/10.1016/j.amjoto.2022.103779 SN - 1532-818X SN - 0196-0709 VL - 44 IS - 2 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Theelke, Luisa A1 - Wilm, Frauke A1 - Marzahl, Christian A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Maier, Andreas A1 - Aubreville, Marc A1 - Breininger, Katharina T1 - Iterative Cross-Scanner Registration for Whole Slide Images T2 - 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) UR - https://doi.org/10.1109/ICCVW54120.2021.00071 KW - Pathology KW - Computer vision KW - Image resolution KW - Microscopy KW - Conferences KW - Estimation KW - Registers Y1 - 2021 UR - https://doi.org/10.1109/ICCVW54120.2021.00071 SN - 978-1-6654-0191-3 SN - 2473-9944 N1 - Eine frei zugängliche Version ist veröffentlicht unter: https://openaccess.thecvf.com/content/ICCV2021W/CDPath/html/Theelke_Iterative_Cross-Scanner_Registration_for_Whole_Slide_Images_ICCVW_2021_paper.html SP - 582 EP - 590 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Aubreville, Marc A1 - Bertram, Christof A1 - Stathonikos, Nikolas A1 - ter Hoeve, Natalie A1 - Ciompi, Francesco A1 - Klopfleisch, Robert A1 - Veta, Mitko A1 - Donovan, Taryn A1 - Marzahl, Christian A1 - Wilm, Frauke A1 - Breininger, Katharina A1 - Maier, Andreas T1 - Quantifying the Scanner-Induced Domain Gap in Mitosis Detection T2 - MIDL: Medical Imaging with Deep Learning 2021 Y1 - 2021 UR - https://2021.midl.io/papers/i6 PB - MIDL Foundation CY - Nijmegen ER - 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 -