@article{SievertOetterAubrevilleetal.2021, author = {Sievert, Matti and Oetter, Nicolai and Aubreville, Marc and Stelzle, Florian and Maier, Andreas and Eckstein, Markus and Mantsopoulos, Konstantinos and Gostian, Antoniu-Oreste and M{\"u}ller, Sarina K. and Koch, Michael and Agaimy, Abbas and Iro, Heinrich and Goncalves, Miguel}, title = {Feasibility of intraoperative assessment of safe surgical margins during laryngectomy with confocal laser endomicroscopy}, volume = {48}, journal = {Auris Nasus Larynx}, subtitle = {a pilot study}, number = {4}, publisher = {Elsevier Science}, address = {Amsterdam}, issn = {1879-1476}, doi = {https://doi.org/10.1016/j.anl.2021.01.005}, pages = {764 -- 769}, year = {2021}, language = {en} } @inproceedings{MarzahlWilmTharunetal.2021, author = {Marzahl, Christian and Wilm, Frauke and Tharun, Lars and Perner, Sven and Kr{\"o}ger, Christine and Voigt, J{\"o}rn and Klopfleisch, Robert and Maier, Andreas and Aubreville, Marc and Breininger, Katharina}, title = {Robust quad-tree based registration on whole slide images}, booktitle = {Proceedings of Machine Learning Research: Proceedings of COMPAY 2021}, number = {156}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v156/marzahl21a.html}, pages = {181 -- 190}, year = {2021}, language = {en} } @inproceedings{GanzKirschHoffmannetal.2021, author = {Ganz, Jonathan and Kirsch, Tobias and Hoffmann, Lucas and Maier, Andreas and Breininger, Katharina and Bl{\"u}mcke, Ingmar and Jabari, Samir and Aubreville, Marc}, title = {Automatic and explainable grading of meningiomas from histopathology images}, volume = {2021}, booktitle = {Proceedings of Machine Learning Research: Proceedings of COMPAY 2021}, number = {156}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v156/ganz21a.html}, pages = {69 -- 80}, year = {2021}, language = {en} } @inproceedings{AubrevilleBertramKlopfleischetal.2019, author = {Aubreville, Marc and Bertram, Christof and Klopfleisch, Robert and Maier, Andreas}, title = {Augmented mitotic cell count using field of interest proposal}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019, Algorithmen - Systeme - Anwendungen, Proceedings des Workshops vom 17. bis 19. M{\"a}rz 2019 in L{\"u}beck}, editor = {Handels, Heinz and Deserno, Thomas Martin and Maier, Andreas and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-25325-7}, doi = {https://doi.org/10.1007/978-3-658-25326-4_71}, pages = {321 -- 326}, year = {2019}, language = {en} } @unpublished{GanzMarzahlAmmelingetal.2024, author = {Ganz, Jonathan and Marzahl, Christian and Ammeling, Jonas and Rosbach, Emely and Richter, Barbara and Puget, Chlo{\´e} and Denk, Daniela and Demeter, Elena A. and Tabaran, Flaviu A. and Wasinger, Gabriel and Lipnik, Karoline and Tecilla, Marco and Valentine, Matthew J. and Dark, Michael and Abele, Niklas and Bolfa, Pompei and Erber, Ramona and Klopfleisch, Robert and Merz, Sophie and Donovan, Taryn and Jabari, Samir and Bertram, Christof and Breininger, Katharina and Aubreville, Marc}, title = {Information Mismatch in PHH3-Assisted Mitosis Annotation Leads to Interpretation Shifts in H\&E Slide Analysis}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-4900505/v1}, year = {2024}, abstract = {The count of mitotic figures (MFs) observed in hematoxylin and eosin (H\&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms' performance. Unlike H\&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H\&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H\&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H\&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models' performance. We found that the annotators' object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H\&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H\&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H\&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.}, language = {en} } @article{AubrevilleBertramMarzahletal.2020, author = {Aubreville, Marc and Bertram, Christof and Marzahl, Christian and Gurtner, Corinne and Dettwiler, Martina and Schmidt, Anja and Bartenschlager, Florian and Merz, Sophie and Fragoso-Garcia, Marco and Kershaw, Olivia and Klopfleisch, Robert and Maier, Andreas}, title = {Deep learning algorithms out-perform veterinary pathologists in detecting the mitotically most active tumor region}, volume = {10}, pages = {16447}, journal = {Scientific reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-020-73246-2}, year = {2020}, abstract = {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.}, language = {en} } @unpublished{AmmelingHeckerGanzetal.2023, author = {Ammeling, Jonas and Hecker, Moritz and Ganz, Jonathan and Donovan, Taryn and Klopfleisch, Robert and Bertram, Christof and Breininger, Katharina and Aubreville, Marc}, title = {Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2311.08949}, year = {2023}, abstract = {The volume-corrected mitotic index (M/V-Index) was shown to provide prognostic value in invasive breast carcinomas. However, despite its prognostic significance, it is not established as the standard method for assessing aggressive biological behaviour, due to the high additional workload associated with determining the epithelial proportion. In this work, we show that using a deep learning pipeline solely trained with an annotation-free, immunohistochemistry-based approach, provides accurate estimations of epithelial segmentation in canine breast carcinomas. We compare our automatic framework with the manually annotated M/V-Index in a study with three board-certified pathologists. Our results indicate that the deep learning-based pipeline shows expert-level performance, while providing time efficiency and reproducibility.}, language = {en} } @inproceedings{AubrevilleGoncalvesKnipferetal.2019, author = {Aubreville, Marc and Goncalves, Miguel and Knipfer, Christian and Oetter, Nicolai and W{\"u}rfl, Tobias and Neumann, Helmut and Stelzle, Florian and Bohr, Christopher and Maier, Andreas}, title = {Transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract}, booktitle = {Biomedical Engineering Systems and Technologies}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-29195-2}, issn = {1865-0929}, doi = {https://doi.org/10.1007/978-3-030-29196-9_4}, pages = {67 -- 85}, year = {2019}, language = {en} } @inproceedings{StoeveAubrevilleOetteretal.2018, author = {Stoeve, Maike and Aubreville, Marc and Oetter, Nicolai and Knipfer, Christian and Neumann, Helmut and Stelzle, Florian and Maier, Andreas}, title = {Motion Artifact Detection in Confocal Laser Endomicroscopy Images}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2018: Algorithmen - Systeme - Anwendungen}, editor = {Maier, Andreas and Deserno, Thomas Martin and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-56537-7}, doi = {https://doi.org/10.1007/978-3-662-56537-7_85}, pages = {328 -- 333}, year = {2018}, 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 = {Mitotic count in canine cutaneous mast cell tumours}, volume = {2020}, journal = {Journal of Comparative Pathology}, subtitle = {not accurate but reproducible}, number = {174}, publisher = {Elsevier}, address = {London}, issn = {1532-3129}, doi = {https://doi.org/10.1016/j.jcpa.2019.10.015}, pages = {143}, year = {2020}, language = {en} } @inproceedings{SteffesAubrevilleSesselmannetal.2018, author = {Steffes, Lara-Maria and Aubreville, Marc and Sesselmann, Stefan and Krenn, Veit and Maier, Andreas}, title = {Classification of Polyethylene Particles and the Local CD3+ Lymphocytosis in Histological Slices}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2018}, editor = {Maier, Andreas and Deserno, Thomas Martin and Handels, Heinz and Maier-Hein, Klaus H. and Palm, Christoph and Tolxdorff, Thomas}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-662-56536-0}, doi = {https://doi.org/10.1007/978-3-662-56537-7_63}, pages = {228 -- 233}, year = {2018}, language = {en} } @unpublished{WilmFragosoGarciaBertrametal.2022, author = {Wilm, Frauke and Fragoso-Garcia, Marco and Bertram, Christof and Stathonikos, Nikolas and {\"O}ttl, Mathias and Qiu, Jingna and Klopfleisch, Robert and Maier, Andreas and Aubreville, Marc and Breininger, Katharina}, title = {Mind the Gap: Scanner-induced domain shifts pose challenges for representation learning in histopathology}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2211.16141}, year = {2022}, language = {en} } @inproceedings{AubrevilleKrappmannBertrametal.2017, author = {Aubreville, Marc and Krappmann, Maximilian and Bertram, Christof and Klopfleisch, Robert and Maier, Andreas}, title = {A Guided Spatial Transformer Network for Histology Cell Differentiation}, booktitle = {VCBM '17: Proceedings of the Eurographics Workshop on Visual Computing for Biology and Medicine}, publisher = {Eurographics Association}, address = {Goslar}, isbn = {978-3-03868-036-9}, doi = {https://doi.org/10.2312/vcbm.20171233}, pages = {21 -- 25}, year = {2017}, language = {en} } @inbook{MuallaAubrevilleMaier2018, author = {Mualla, Firas and Aubreville, Marc and Maier, Andreas}, title = {Microscopy}, booktitle = {Medical Imaging Systems: An Introductory Guide}, editor = {Maier, Andreas and Steidl, Stefan and Christlein, Vincent and Hornegger, Joachim}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-96520-8}, doi = {https://doi.org/10.1007/978-3-319-96520-8_5}, pages = {69 -- 90}, year = {2018}, abstract = {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.}, language = {en} } @unpublished{AmmelingSchmidtGanzetal.2022, author = {Ammeling, Jonas and Schmidt, Lars-Henning and Ganz, Jonathan and Niedermair, Tanja and Brochhausen-Delius, Christoph and Schulz, Christian and Breininger, Katharina and Aubreville, Marc}, title = {Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2212.07724}, year = {2022}, abstract = {Attention-based multiple instance learning (AMIL) algorithms have proven to be successful in utilizing gigapixel whole-slide images (WSIs) for a variety of different computational pathology tasks such as outcome prediction and cancer subtyping problems. We extended an AMIL approach to the task of survival prediction by utilizing the classical Cox partial likelihood as a loss function, converting the AMIL model into a nonlinear proportional hazards model. We applied the model to tissue microarray (TMA) slides of 330 lung cancer patients. The results show that AMIL approaches can handle very small amounts of tissue from a TMA and reach similar C-index performance compared to established survival prediction methods trained with highly discriminative clinical factors such as age, cancer grade, and cancer stage.}, language = {en} } @unpublished{StoeveAubrevilleOetteretal.2018, author = {Stoeve, Maike and Aubreville, Marc and Oetter, Nicolai and Knipfer, Christian and Neumann, Helmut and Stelzle, Florian and Maier, Andreas}, title = {Motion Artifact Detection in Confocal Laser Endomicroscopy Images}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1711.01117}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{AubrevilleBertramKlopfleischetal.2018, author = {Aubreville, Marc and Bertram, Christof and Klopfleisch, Robert and Maier, Andreas}, title = {SlideRunner}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2018: Algorithmen - Systeme - Anwendungen}, subtitle = {a tool for massive cell annotations in whole slide images}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-56537-7}, doi = {https://doi.org/10.1007/978-3-662-56537-7_81}, pages = {309 -- 314}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{MarzahlBertramAubrevilleetal.2020, author = {Marzahl, Christian and Bertram, Christof and Aubreville, Marc and Petrick, Anne and Weiler, Kristina and Gl{\"a}sel, Agnes C. and Fragoso-Garcia, Marco and Merz, Sophie and Bartenschlager, Florian and Hoppe, Judith and Langenhagen, Alina and Jasensky, Anne-Katherine and Voigt, J{\"o}rn and Klopfleisch, Robert and Maier, Andreas}, title = {Are Fast Labeling Methods Reliable? A Case Study of Computer-Aided Expert Annotations on Microscopy Slides}, booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2020}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-59710-8}, issn = {1611-3349}, doi = {https://doi.org/10.1007/978-3-030-59710-8_3}, pages = {24 -- 32}, year = {2020}, language = {en} } @inproceedings{SchroeterRosenkranzEscalanteBetal.2020, author = {Schr{\"o}ter, H. and Rosenkranz, Tobias and Escalante-B, A. N. and Aubreville, Marc and Maier, Andreas}, title = {CLCNET: deep learning-based noise reduction for hearing aids using complex linear coding}, booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-6631-5}, doi = {https://doi.org/10.1109/ICASSP40776.2020.9053563}, pages = {6949 -- 6953}, year = {2020}, language = {en} } @inproceedings{MarzahlAubrevilleVoigtetal.2019, author = {Marzahl, Christian and Aubreville, Marc and Voigt, J{\"o}rn and Maier, Andreas}, title = {Classification of leukemic b-lymphoblast cells from blood smear microscopic images with an attention-based deep learning method and advanced augmentation techniques}, booktitle = {ISBI 2019 C-NMC challenge: classification in cancer cell imaging}, publisher = {Springer}, address = {Singapore}, isbn = {978-981-15-0797-7}, issn = {2195-271X}, doi = {https://doi.org/10.1007/978-981-15-0798-4_2}, pages = {13 -- 22}, year = {2019}, language = {en} } @unpublished{AubrevillePanSievertetal.2023, author = {Aubreville, Marc and Pan, Zhaoya and Sievert, Matti and Ammeling, Jonas and Ganz, Jonathan and Oetter, Nicolai and Stelzle, Florian and Frenken, Ann-Kathrin and Breininger, Katharina and Goncalves, Miguel}, title = {Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2311.07216}, year = {2023}, abstract = {The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections. Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has shown its potential in the live optical biopsy of tissue. An automated analysis of this notoriously difficult to interpret modality would help surgeons. However, the images of CLE show a wide variability of patterns, caused both by individual factors but also, and most strongly, by the anatomical structures of the imaged tissue, making it a challenging pattern recognition task. In this work, we evaluate four popular few shot learning (FSL) methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme. The best respective approach reached a median accuracy of 79.6\% on the rather homogeneous VF dataset, but only of 61.6\% for the highly diverse SNT dataset. Our results indicate that FSL on CLE images is viable, but strongly affected by the number of patients, as well as the diversity of anatomical patterns.}, language = {en} } @inproceedings{BertramVetaMarzahletal.2020, author = {Bertram, Christof and Veta, Mitko and Marzahl, Christian and Stathonikos, Nikolas and Maier, Andreas and Klopfleisch, Robert and Aubreville, Marc}, title = {Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels}, booktitle = {Interpretable and Annotation-Efficient Learning for Medical Image Computing}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-61166-8}, issn = {1611-3349}, doi = {https://doi.org/10.1007/978-3-030-61166-8_22}, pages = {204 -- 213}, year = {2020}, language = {en} }