@inproceedings{MarzahlAubrevilleBertrametal.2020, author = {Marzahl, Christian and Aubreville, Marc and Bertram, Christof and Gerlach, Stefan and Maier, Jennifer and Voigt, J{\"o}rn and Hill, Jenny and Klopfleisch, Robert and Maier, Andreas}, title = {Is crowd-algorithm collaboration an advanced alternative to crowd-sourcing on cytology slides?}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2020, Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, editor = {Tolxdorff, Thomas and Deserno, Thomas Martin and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-29266-9}, doi = {https://doi.org/10.1007/978-3-658-29267-6_5}, pages = {26 -- 31}, year = {2020}, language = {en} } @article{MarzahlAubrevilleBertrametal.2021, author = {Marzahl, Christian and Aubreville, Marc and Bertram, Christof and Maier, Jennifer and Bergler, Christian and Kr{\"o}ger, Christine and Voigt, J{\"o}rn and Breininger, Katharina and Klopfleisch, Robert and Maier, Andreas}, title = {EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control}, volume = {11}, pages = {4343}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-021-83827-4}, year = {2021}, abstract = {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.}, language = {en} } @article{GoncalvesAubrevilleMuelleretal.2019, author = {Goncalves, Miguel and Aubreville, Marc and M{\"u}ller, Sarina K. and Sievert, Matti and Maier, Andreas and Iro, Heinrich and Bohr, Christopher}, title = {Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds}, volume = {39}, journal = {ACTA Otorhinolaryngologica Italica}, number = {6}, publisher = {Pacini}, address = {Pisa}, issn = {1827-675X}, doi = {https://doi.org/10.14639/0392-100X-2121}, pages = {389 -- 395}, year = {2019}, abstract = {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.}, language = {en} } @article{BertramAubrevilleGurtneretal.2020, author = {Bertram, Christof and Aubreville, Marc and Gurtner, Corinne and Bartel, Alexander and Corner, Sarah M. and Dettwiler, Martina and Kershaw, Olivia and Noland, Erica L. and Schmidt, Anja and Sledge, Dodd G. and Smedley, Rebecca C. and Thaiwong, Tuddow and Kiupel, Matti and Maier, Andreas and Klopfleisch, Robert}, title = {Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent}, volume = {57}, journal = {Veterinary Pathology}, number = {2}, publisher = {Sage}, address = {London}, issn = {1544-2217}, doi = {https://doi.org/10.1177/0300985819890686}, pages = {214 -- 226}, year = {2020}, language = {en} } @inproceedings{AubrevilleBertramJabarietal.2020, author = {Aubreville, Marc and Bertram, Christof and Jabari, Samir and Marzahl, Christian and Klopfleisch, Robert and Maier, Andreas}, title = {Inter-species, inter-tissue domain adaptation for mitotic figure assessment}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2020, Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2020 in Berlin}, subtitle = {learning new tricks from old dogs}, editor = {Tolxdorff, Thomas and Deserno, Thomas Martin and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-29266-9}, doi = {https://doi.org/10.1007/978-3-658-29267-6_1}, pages = {1 -- 7}, year = {2020}, language = {en} } @article{AubrevilleKnipferOetteretal.2017, author = {Aubreville, Marc and Knipfer, Christian and Oetter, Nicolai and Jaremenko, Christian and Rodner, Erik and Denzler, Joachim and Bohr, Christopher and Neumann, Helmut and Stelzle, Florian and Maier, Andreas}, title = {Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning}, volume = {7}, pages = {11979}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-017-12320-8}, year = {2017}, abstract = {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\%).}, language = {en} } @inproceedings{AubrevilleBertramKlopfleischetal.2019, author = {Aubreville, Marc and Bertram, Christof and Klopfleisch, Robert and Maier, Andreas}, title = {Field of Interest Proposal for Augmented Mitotic Cell Count}, volume = {2}, booktitle = {Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING}, subtitle = {Comparison of Two Convolutional Networks}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-353-7}, issn = {2184-4305}, doi = {https://doi.org/10.5220/0007365700300037}, pages = {30 -- 37}, year = {2019}, language = {en} } @article{AubrevilleStoeveOetteretal.2019, author = {Aubreville, Marc and Stoeve, Maike and Oetter, Nicolai and Goncalves, Miguel and Knipfer, Christian and Neumann, Helmut and Bohr, Christopher and Stelzle, Florian and Maier, Andreas}, title = {Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images}, volume = {14}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {1}, publisher = {Springer}, address = {Berlin}, issn = {1861-6429}, doi = {https://doi.org/10.1007/s11548-018-1836-1}, pages = {31 -- 42}, year = {2019}, language = {en} } @article{BertramAubrevilleDonovanetal.2021, author = {Bertram, Christof and Aubreville, Marc and Donovan, Taryn and Bartel, Alexander and Wilm, Frauke and Marzahl, Christian and Assenmacher, Charles-Antoine and Becker, Kathrin and Bennett, Mark and Corner, Sarah M. and Cossic, Brieuc and Denk, Daniela and Dettwiler, Martina and Garcia Gonzalez, Beatriz and Gurtner, Corinne and Haverkamp, Ann-Kathrin and Heier, Annabelle and Lehmbecker, Annika and Merz, Sophie and Noland, Erica L. and Plog, Stephanie and Schmidt, Anja and Sebastian, Franziska and Sledge, Dodd G. and Smedley, Rebecca C. and Tecilla, Marco and Thaiwong, Tuddow and Fuchs-Baumgartinger, Andrea and Meuten, Donald J. and Breininger, Katharina and Kiupel, Matti and Maier, Andreas and Klopfleisch, Robert}, title = {Computer-assisted mitotic count using a deep learning-based algorithm improves interobserver reproducibility and accuracy}, volume = {59}, journal = {Veterinary Pathology}, number = {2}, publisher = {Sage}, address = {London}, issn = {1544-2217}, doi = {https://doi.org/10.1177/03009858211067478}, pages = {211 -- 226}, year = {2021}, abstract = {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.}, language = {en} } @article{MarzahlAubrevilleBertrametal.2020, author = {Marzahl, Christian and Aubreville, Marc and Bertram, Christof and Stayt, Jason and Jasensky, Anne-Katherine and Bartenschlager, Florian and Fragoso-Garcia, Marco and Barton, Ann K. and Elsemann, Svenja and Jabari, Samir and Krauth, Jens and Madhu, Prathmesh and Voigt, J{\"o}rn and Hill, Jenny and Klopfleisch, Robert and Maier, Andreas}, title = {Deep Learning-based quantification of pulmonary hemosiderophages in cytology slides}, volume = {10}, pages = {9795}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-020-65958-2}, year = {2020}, abstract = {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.}, language = {en} }