Our research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.
Research projects
Image Analysis and Fusion
Learning Algorithms for Medical Big Data Analysis (LAMBDA)
Magnetic Resonance Imaging (MRI)
Speech Processing and Understanding
Development of a guideline for the three-dimensional non-destructive acquisition of manuscripts
Intelligent MR Diagnosis of the Liver by Linking Model and Data-driven Processes (iDELIVER)
Molecular Assessment of Signatures ChAracterizing the Remission of Arthritis
Improved dual energy imaging using machine learning
Font Group Recognition for Improved OCR
(Third Party Funds Single)
Term: 1. August 2021 - 1. August 2023 Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Although OCR-D made huge progress in the last project phase in providing OCR for early printed books, it still faces two major problems: The huge variety of the material makes it extremely challenging to use generic OCR-models. Yet, selecting specific models is not possible as the sheer amount of material prevents a fully automatic workflow. This situation is further complicated by the lack of appropriate OCR training data. Current data sets consist overwhelmingly of texts in Fraktur, especially from the 19th century. This completely neglects the large typographic variety displayed by printing in the three previous centuries. Therefore, and in response to the demand from SLUB Dresden and ULB Halle, we propose to improve the current situation significantly1) fine tuning our font group recognition system to such a degree that it can be used at character level;2) transcribing more specific OCR training data for the 16th-18th century, which includes popular fonts such as Schwabacher, other bastards and old Fraktur styles; 3) training font-specific OCR models as well as integrated models that recognise both typeface and text simultaneously. This approach has ensured in other contexts that the network performs better on both individual tasks, as we can thus reduce overfitting during training. This project will improve OCR quality significantly, especially for books in non-Fraktur fonts. It will also provide a training data set of very high quality that can be reused in long term. Finally, the project will provide a more fine-grained font recognition tool that, beyond enabling font-specific OCR, also has important applications in text attribute recognition and layout analysis.
Our senses are gateways to the past. Although museums are slowly discovering the power of multi-sensory presentations, we lack the scientific standards, tools and data to identify, consolidate, and promote the wide-ranging role of scents and smelling in our cultural heritage. In recent years, European cultural heritage institutions have invested heavily in large-scale digitization. A wealth of object, text and image data that can be analysed using computer science techniques now exists. However, the potential olfactory descriptions, experiences, and memories that they contain remain unexplored. We recognize this as both a challenge and an opportunity. Odeuropa will apply state-of-the-art AI techniques to text and image datasets that span four centuries of European history. It will identify the vocabularies, spaces, events, practices, and emotions associated with smells and smelling. The project will curate this multi-modal information, following semantic web standards, and store the enriched data in a ‘European Olfactory Knowledge Graph’ (EOKG). We will use this data to identify ‘storylines’, informed by cultural history and heritage research, and share these with different audiences in different formats: through demonstrators, an online catalogue, toolkits and training documentation describing best-practices in olfactory museology. New, evidence-based methodologies will quantify the impact of multisensory visitor engagement. This data will support the implementation of policy recommendations for recognising, promoting, presenting and digitally preserving olfactory heritage. These activities will realize Odeuropa’s main goal: to show that smells and smelling are important and viable means for consolidating and promoting Europe’s tangible and intangible cultural heritage.
The project examines the use and further development of machine learning methods for MR image reconstruction and for the classification of liver lesions. Based on a comparison model and data-driven image reconstruction methods, these are to be systematically linked in order to enable high acceleration without sacrificing diagnostic value. In addition to the design of suitable networks, research should also be carried out to determine whether metadata (e.g. age of the patient) can be incorporated into the reconstruction. Furthermore, suitable classification algorithms on an image basis are to be developed and the potential of direct classification on the raw data is to be explored. In the long term, intelligent MR diagnostics can significantly increase the efficiency of use of MR hardware, guarantee better patient care and set new impulses in medical technology.
MASCARA zielt auf eine detaillierte, molekulare Charakterisierung der Remission bei Arthritis ab. Das Projekt basiert auf der kombinierten klinischen und technischen Erfahrung von Rheumatologen, Radiologen, Medizinphysikern, Nuklearmedizinern, Gastroenterologen, grundlagenwissenschaftlichen Biologen und Informatikern und verbindet fünf akademische Fachzentren in Deutschland. Das Projekt adressiert 1) den Umstand der zunehmenden Zahl von Arthritis Patienten in Remission, 2) die Herausforderungen, eine effektive Unterdrückung der Entzündung von einer Heilung zu unterscheiden und 3) das begrenzte Wissen über die Gewebeveränderungen in den Gelenken von Patienten mit Arthritis. MASCARA wird auf der Grundlage vorläufiger Daten vier wichtige mechanistische Bereiche (immunstoffwechselbedingte Veränderungen, mesenchymale Gewebereaktionen, residente Immunzellen und Schutzfunktion des Darms) untersuchen, die gemeinsam den molekularen Zustand der Remission bestimmen. Das Projekt zielt auf die Sammlung von Synovialbiopsien und die anschließende Gewebeanalyse bei Patienten mit aktiver Arthritis und Patienten in Remission ab. Die Gewebeanalysen umfassen (Einzelzell)-mRNA-Sequenzierung, Massenzytometrie sowie die Messung von Immunmetaboliten und werden durch molekulare Bildgebungsverfahren wie CEST-MRT und FAPI-PET ergänzt. Sämtliche Daten, die in dem Vorhaben generiert werden, werden in einem bereits bestehenden Datenbanksystem mit den Daten der anderen Partner zusammengeführt und gespeichert. Das Zusammenführen der Daten soll – mit Hilfe von maschinellem Lernen – krankheitsspezifische und mit der Krankheitsaktivität verbundene Mustermatrizen identifizieren.
This project aims to improve multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information. Such improvements include noise reduction and artifact removal from data acquired in SPECT.
Due to Europe's ageing society, there has been a dramatic increase in the occurrence of osteoporosis (OP) and related diseases. Sufferers have an impaired quality of life, and there is a considerable cost to society associated with the consequent loss of productivity and injuries. The current understanding of this disease needs to be revolutionized, but study has been hampered by a lack of means to properly characterize bone structure, remodeling dynamics and vascular activity. This project, 4D nanoSCOPE, will develop tools and techniques to permit time-resolved imaging and characterization of bone in three spatial dimensions (both in vitro and in vivo), thereby permitting monitoring of bone remodeling and revolutionizing the understanding of bone morphology and its function.
To advance the field, in vivo high-resolution studies of living bone are essential, but existing techniques are not capable of this. By combining state-of-the art image processing software with innovative 'precision learning' software methods to compensate for artefacts (due e.g. to the subject breathing or twitching), and innovative X-ray microscope hardware which together will greatly speed up image acquisition (aim is a factor of 100), the project will enable in vivo X-ray microscopy studies of small animals (mice) for the first time. The time series of three-dimensional X-ray images will be complemented by correlative microscopy and spectroscopy techniques (with new software) to thoroughly characterize (serial) bone sections ex vivo.
The resulting three-dimensional datasets combining structure, chemical composition, transport velocities and local strength will be used by the PIs and international collaborators to study the dynamics of bone microstructure. This will be the first time that this has been possible in living creatures, enabling an assessment of the effects on bone of age, hormones, inflammation and treatment.
Reduction of unwanted environmental noises is an important feature of today’s hearing aids, which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. Due to the large number of different background noises in daily situations, it is hard to heuristically cover the complete solution space of noise reduction schemes. Deep learning-based algorithms pose a possible solution to this dilemma, however, they sometimes lack robustness and applicability in the strict context of hearing aids. In this project we investigate several deep learning.based methods for noise reduction under the constraints of modern hearing aids. This involves a low latency processing as well as the employing a hearing instrument-grade filter bank. Another important aim is the robustness of the developed methods. Therefore, the methods will be applied to real-world noise signals recorded with hearing instruments.
Deep Learning Applied to Animal Linguistics in particular the analysis of underwater audio recordings of marine animals (killer whales):
For marine biologists, the interpretation and understanding of underwater audio recordings is essential. Based on such recordings, possible conclusions about behaviour, communication and social interactions of marine animals can be made. Despite a large number of biological studies on the subject of orca vocalizations, it is still difficult to recognize a structure or semantic/syntactic significance of orca signals in order to be able to derive any language and/or behavioral patterns. Due to a lack of techniques and computational tools, hundreds of hours of underwater recordings are still manually verified by marine biologists in order to detect potential orca vocalizations. In a post process these identified orca signals are analyzed and categorized. One of the main goals is to provide a robust and automatic method which is able to automatically detect orca calls within underwater audio recordings. A robust detection of orca signals is the baseline for any further and deeper analysis. Call type identification and classification based on pre-segmented signals can be used in order to derive semantic and syntactic patterns. In connection with the associated situational video recordings and behaviour descriptions (provided by several researchers on site) can provide potential information about communication (kind of a language model) and behaviors (e.g. hunting, socializing). Furthermore, orca signal detection can be used in conjunction with a localization software in order to provide researchers on the field with a more efficient way of searching the animals as well as individual recognition.
For more information about the DeepAL project please contact christian.bergler@fau.de.
Yoon, S.S., Fischer, C., Toupin, S., Pezel, T., Garot, J., Wetzl, J., & Maier, A. (2022). Fully automatic AI-based extraction of mitral valve motion parameters on long axis CINE images – validation and application on N=2000 patient datasets. Paper presentation at Society for Cardiovascular Magnetic Resonance 25th Annual Scientific Sessions, Fort Lauderdale, US.
Bertram, C.A., Donovan, T.A., Tecilla, M., Bartenschlager, F., Fragoso, M., Wilm, F.,... Aubreville, M. (2021). Dataset on Bi- and Multi-nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 134-139). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Biendl, M., Sindel, A., Klinke, T., Maier, A., & Christlein, V. (2021). Automatic Chain Line Segmentation in Historical Prints. In Pattern Recognition. ICPR International Workshops and Challenges (pp. 657-665). Online (Milan, Italy): Springer, Cham.
Chen, S., Stromer, D., Alnasser Alabdalrahim, H., Schwab, S., Weih, M., & Maier, A. (2021). Abstract: Automatic Dementia Screening and Scoring by Applying Deep Learning on Clock-drawing Tests. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 289-). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Christlein, V., Weichselbaumer, N., Limbach, S., & Seuret, M. (2021). Proof of Concept: Automatic Type Recognition. In Reussner RH, Koziolek A, Heinrich R (Eds.), INFORMATIK 2020 (pp. 1307-1316). Gesellschaft für Informatik, Bonn.
Denzinger, F., Wels, M., Breininger, K., Gülsün, M.A., Schöbinger, M., André, F.,... Maier, A. (2021). Abstract: Automatic CAD-RADS Scoring using Deep Learning. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 104-). Regensburg, DEU: Springer Science and Business Media Deutschland GmbH.
Denzinger, F., Wels, M., Hopfgartner, C., Lu, J., Schöbinger, M., Maier, A., & Sühling, M. (2021). Coronary Plaque Analysis for CT Angiography Clinical Research. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 223-228). Regensburg, DEU: Springer Science and Business Media Deutschland GmbH.
Felsner, L., Syben, C., Maier, A., & Riess, C. (2021). Helical Dark-field Fiber Reconstruction. In Proceedings of the International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D).
Fu, W., Mill, L., Seitz, S., Geimer, T., Kling, L., Possart, D.,... Maier, A. (2021). Towards Mouse Bone X-ray Microscopy Scan Simulation. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 128-133). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Hoffmann, M., Hepp, J., Doll, B., Buerhop-Lutz, C., Peters, I.M., Brabec, C.,... Christlein, V. (2021). Module-Power Prediction from PL Measurements using Deep Learning. In Proceedings of the IEEE Photovoltaic Specialists Conference. Fort Lauderdale, FL, USA: IEEE.
Hoppe, E., Wetzl, J., Roser, P., Felsner, L., Preuhs, A., & Maier, A. (2021). 2D Respiration Navigation Framework for 3D Continuous Cardiac Magnetic Resonance Imaging. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 158-163). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Husvogt, L., Ploner, S., Chen, S., Stromer, D., Schottenhamml, J., Alibhai, Y.,... Maier, A. (2021). Abstract: Maximum A-posteriori Signal Recovery for OCT Angiography Image Generation. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 261-). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Jaganathan, S., Wang, J., Borsdorf, A., & Maier, A. (2021). Learning the Update Operator for 2D/3D Image Registration. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 117-122). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Kedilioglu, O., Lieret, M., Schottenhamml, J., Würfl, T., Blank, A., Maier, A., & Franke, J. (2021). RGB-D-based Human Detection and Segmentation for Mobile Robot Navigation in Industrial Environments. In Giovanni Maria Farinella, Petia Radeva, Jose Braz, Kadi Bouatouch (Eds.), Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) (pp. 219-226). SciTePress.
Maier, J., Nitschke, M., Choi, J.H., Gold, G., Fahrig, R., Eskofier, B., & Maier, A. (2021). Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 336-). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Maier, J., Schottenhamml, J., Madhu, P., da Costa, C.A., & Maier, A. (2021). Analysis of Interventional Workflow Phases based on Image Classification. In 65th Annual Meeting of the German Association for Medical Informatics, Biometry and Epidemiology (GMDS). Berlin (online conference), DE.
Martín Vicario, C., Kordon, F.J., Denzinger, F., Weiten, M., Thomas, S., Kausch, L.,... Kunze, H. (2021). Automatic Plane Adjustment in Surgical Cone Beam CT-volumes. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 170-). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Marzahl, C., Aubreville, M., Bertram, C.A., Stayt, J., Jasensky, A.K., Bartenschlager, F.,... Maier, A. (2021). Abstract: Deep Learning-based Quantification of Pulmonary Hemosiderophages in Cytology Slides. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 48-). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Marzahl, C., Bertram, C.A., Wilm, F., Voigt, J., Barton, A.K., Klopfleisch, R.,... Aubreville, M. (2021). Cell Detection for Asthma on Partially Annotated Whole Slide Images: Learning to be EXACT. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 147-152). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Mattick, A., Mayr, M., Seuret, M., Maier, A., & Christlein, V. (2021). SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators. In Josep Lladós, Daniel Lopresti, Seiichi Uchida (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 268-283). Lausanne, CHE: Springer Science and Business Media Deutschland GmbH.
Meister, F., Passerini, T., Audigier, C., Lluch, È., Mihalef, V., Ashikaga, H.,... Mansi, T. (2021). Graph Convolutional Regression of Cardiac Depolarization from Sparse Endocardial Maps. In Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young (Eds.), Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. (pp. 23-34).
Naderi Boldaji, H., Patwari, M., Reymann, M., Gutjahr, R., Raupach, R., & Maier, A. (2021). Deep Learning based Model Observers for Multi - Modal Imaging. In Proceedings of the 16th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine. Leuven, Belgium, BE.
Pérez Toro, P.A., Vasquez Correa, J., Arias-Vergara, T., Klumpp, P., Sierra-Castrillón, M., Roldán-López, M.E.,... Nöth, E. (2021). Acoustic and Linguistic Analyses to Assess Early-Onset and Genetic Alzheimer's Disease. In Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8338-8342). IEEE.
Rajput, J.R., Shetty, K., Maier, A., & Berger, M. (2021). Table Motion Detection in Interventional Coronary Angiography. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 55-60). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Reimann, M., Fu, W., & Maier, A. (2021). Novel Evaluation Metrics for Vascular Structure Segmentation. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 80-85). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Rios-Urrego, C.D., Vásquez-Correa, J.C., Orozco-Arroyave, J.R., & Nöth, E. (2021). Is There Any Additional Information in a Neural Network Trained for Pathological Speech Classification? In Kamil Ekštein, František Pártl, Miloslav Konopík (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 435-447). Olomouc, CZ: Springer Science and Business Media Deutschland GmbH.
Roser, P., Felsner, L., Maier, A., & Riess, C. (2021). Learning the Inverse Weighted Radon Transform. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 49-54). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Roser, P., Zhong, X., Birkhold, A., Preuhs, A., Syben, C., Hoppe, E.,... Maier, A. (2021). Abstract: Simultaneous Estimation of X-ray Back-scatter and Forward-scatter using Multi-task Learning. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 262-). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Seuret, M., Nicolaou, A., Rodríguez-Salas, D., Weichselbaumer, N., Stutzmann, D., Mayr, M.,... Christlein, V. (2021). ICDAR 2021 Competition on Historical Document Classification. In Josep Lladós, Daniel Lopresti, Seiichi Uchida (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 618-634). Lausanne, CHE: Springer Science and Business Media Deutschland GmbH.
Shetty, K., Birkhold, A., Strobel, N., Egger, B., Jaganathan, S., Kowarschik, M., & Maier, A. (2021). Deep Learning Compatible Differentiable X-ray Projections for Inverse Rendering. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 290-295). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Tripathi, P., Obler, R., Maier, A., & Janssen, H. (2021). A Novel Trilateral Filter for Digital Subtraction Angiography. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 310-315). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Wallraff, S., Vesal, S., Syben, C., Lutz, R., & Maier, A. (2021). Age Estimation on Panoramic Dental X-ray Images using Deep Learning. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 186-191). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Wilm, F., Bertram, C.A., Marzahl, C., Bartel, A., Donovan, T.A., Assenmacher, C.A.,... Aubreville, M. (2021). Influence of Inter-Annotator Variability on Automatic Mitotic Figure Assessment. In Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 241-246). Regensburg, DE: Springer Science and Business Media Deutschland GmbH.
Ahmed, J., Vesal, S., Durlak, F., Kaergel, R., Ravikumar, N., Rémy-Jardin, M., & Maier, A. (2020). COPD classification in CT images using a 3D convolutional neural network. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 39-45). Berlin, DE: Springer.
Argüello-Vélez, P., Arias-Vergara, T., González-Rátiva, M.C., Orozco-Arroyave, J.R., Nöth, E., & Schuster, M.E. (2020). Acoustic characteristics of vot in plosive consonants produced by parkinson’s patients. In Petr Sojka, Ivan Kopecek, Karel Pala, Aleš Horák (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 303-311). Brno, CZ: Springer Science and Business Media Deutschland GmbH.
Bergler, C., Schmitt, M., Maier, A., Smeele, S., Barth, V., & Nöth, E. (2020). ORCA-CLEAN: A Deep Denoising Toolkit for Killer Whale Communication. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 (pp. 1136-1140). Shanghai, China: International Speech Communication Association.
Bertram, C.A., Veta, M., Marzahl, C., Stathonikos, N., Maier, A., Klopfleisch, R., & Aubreville, M. (2020). Are Pathologist-Defined Labels Reproducible? Comparison of the TUPAC16 Mitotic Figure Dataset with an Alternative Set of Labels. In Jaime Cardoso, Wilson Silva, Ricardo Cruz, Hien Van Nguyen, Badri Roysam, Nicholas Heller, Pedro Henriques Abreu, Jose Pereira Amorim, Ivana Isgum, Vishal Patel, Kevin Zhou, Steve Jiang, Ngan Le, Khoa Luu, Raphael Sznitman, Veronika Cheplygina, Samaneh Abbasi, Diana Mateus, Emanuele Trucco (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 204-213). Lima, PE: Springer Science and Business Media Deutschland GmbH.
Bootwala, A., Breininger, K., Maier, A., & Christlein, V. (2020). Assistive Diagnosis in Opthalmology Using Deep Learning-Based Image Retrieval. In Thomas Tolxdorff; Thomas M. Deserno; Heinz Handels; Andreas Maier; Klaus H. Maier-Hein; Christoph Palm (Eds.), Bildverarbeitung für die Medizin 2020. (pp. 144-149). Wiesbaden: Springer Vieweg.
Breininger, K., Pfister, M., Kowarschik, M., & Maier, A. (2020). Move Over There: One-Click Deformation Correction for Image Fusion During Endovascular Aortic Repair. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 713-723). Lima, PE: Springer Science and Business Media Deutschland GmbH.
Felsner, L., Würfl, T., Syben, C., Roser, P., Preuhs, A., Maier, A., & Riess, C. (2020). Reconstruction of Voxels with Position- and Angle-Dependent Weightings. In Proceedings of the The 6th International Conference on Image Formation in X-Ray Computed Tomography. Online meeting.
Fu, W., Husvogt, L., Ploner, S., Fujimoto, J.G., & Maier, A. (2020). Modularization of deep networks allows cross-modality reuse: lesson learnt. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 274-279). Berlin, DE: Springer.
Gadjimuradov, F., Benkert, T., Nickel, M.D., & Maier, A. (2020). Deep Recurrent Partial Fourier Reconstruction in Diffusion MRI. In Farah Deeba, Patricia Johnson, Tobias Würfl, Jong Chul Ye (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 38-47). Lima, PE: Springer Science and Business Media Deutschland GmbH.
Gouet-Brunet, V., Khokhlova, M., Kosti, R.V., Chen, L., & Yin, X.C. (2020). SUMAC 20 Chairs Welcome. In SUMAC 2020 - Proceedings of the 2nd Workshop on Structuring and Understanding of Multimedia heritAge Contents (pp. iii-). Association for Computing Machinery, Inc.
Gündel, S., & Maier, A. (2020). Epoch-wise label attacks for robustness against label noise. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Bildverarbeitung für die Medizin 2020 (pp. 287-292). Springer Vieweg.
Hoßbach, J., Husvogt, L., Kraus, M., Fujimoto, J.G., & Maier, A. (2020). Deep OCT angiography image generation for motion artifact suppression. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 248-253). Berlin, DE: Springer.
Huang, Y., Gao, L., Preuhs, A., & Maier, A. (2020). Field of View Extension in Computed Tomography Using Deep Learning Prior. In Andreas Maier, Klaus Hermann Maier-Hein, Thomas Martin Deserno, Heinz Handels, Thomas Tolxdorff (Eds.), Bildverarbeitung für die Medizin: Algorithmen – Systeme – Anwendungen. Berlin, DE: Springer.
Husvogt, L., Ploner, S., Stromer, D., Schottenhamml, J., Moult, E., Fujimoto, J.G., & Maier, A. (2020). Compressed sensing for optical coherence tomography angiography volume generation. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 82-87). Berlin, DE: Springer.
Klumpp, P., Arias-Vergara, T., Vasquez Correa, J., Pérez Toro, P.A., Hönig, F.T., Nöth, E., & Orozco-Arroyave, J.-R. (2020). Surgical mask detection with deep recurrent phonetic models. In Proceedings of the 21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 (pp. 2057-2061). International Speech Communication Association.
Kordon, F.J., Fischer, P., Privalov, M., Swartman, B., Schnetzke, M., Franke, J.,... Kunze, H. (2020, February). Multi-Task Framework for X-Ray Guided Planning in Knee Surgery. Poster presentation at Bildverarbeitung für die Medizin 2020, Berlin, DE.
Maier, J., Nitschke, M., Choi, J.-H., Gold, G., Fahrig, R., Eskofier, B., & Maier, A. (2020). Inertial Measurements for Motion Compensation in Weight-Bearing Cone-Beam CT of the Knee. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. 14-23).
Maier, J., Rivera Monroy, L., Syben, C., Jeon, Y., Choi, J.-H., Hall, M.,... Maier, A. (2020). Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-Beam CT. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Bildverarbeitung für die Medizin 2020. (pp. 67-72). Wiesbaden: Springer Vieweg.
Marzahl, C., Bertram, C.A., Aubreville, M., Petrick, A., Weiler, K., Gläsel, A.C.,... Maier, A. (2020). Are fast labeling methods reliable? a case study of computer-aided expert annotations on microscopy slides. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 24-32). Lima, PE: Springer Science and Business Media Deutschland GmbH.
Mayr, M., Stumpf, M., Nicolaou, A., Seuret, M., Maier, A., & Christlein, V. (2020). Spatio-Temporal Handwriting Imitation. In Springer, Cham (Eds.), Proceedings of the European Conference on Computer Vision (pp. 528-543). Online.
Meister, F., Houle, H., Nita, C., Puiu, A., Mihai Itu, L., & Rapaka, S. (2020). Additional clinical applications. In Tommaso Mansi, Tiziano Passerini, Dorin Comaniciu (Eds.), Artificial Intelligence for Computational Modeling of the Heart. (pp. 183-210).
Mihai Itu, L., Meister, F., Sharma, P., & Passerini, T. (2020). Data-driven reduction of cardiac models. In Tommaso Mansi, Tiziano Passerini and Dorin Comaniciu (Eds.), Artificial Intelligence for Computational Modeling of the Heart. (pp. 117-160).
Patwari, M., Gutjahr, R., Raupach, R., & Maier, A. (2020). JBFnet - Low Dose CT Denoising by Trainable Joint Bilateral Filtering. In Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (Eds.), Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Surgery (MICCAI) (pp. 506 - 515). Lima, Peru, PE: Springer.
Preuhs, A., Manhart, M., Roser, P., Stimpel, B., Syben, C., Psychogios, M.,... Maier, A. (2020). Deep autofocus with cone-beam CT consistency constraint. In Proceedings of the Bildverarbeitung für die Medizin. Berlin, DE.
Restrepo-Uribe, J.P., Roldan-Vasco, S., Perez-Giraldo, E., Orozco Arroyave, J.R., & Orozco-Duque, A. (2020). Electrophysiological and Mechanical Approaches to the Swallowing Analysis. In Juan Carlos Figueroa-García, Fabián Steven Garay-Rairán, Germán Jairo Hernández-Pérez, Yesid Díaz-Gutierrez (Eds.), Communications in Computer and Information Science (pp. 281-290). Bogota, CO: Springer Science and Business Media Deutschland GmbH.
Reymann, M., Massanes, F., Ritt, P., Cachovan, M., Kuwert, T., Vija, A.H., & Maier, A. (2020, November). Feature Loss After Denoising of SPECT Projection Data using a U-Net. Poster presentation at 2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, Boston, Massachusetts, US.
Roser, P., Zhong, X., Birkhold, A., Preuhs, A., Syben, C., Hoppe, E.,... Maier, A. (2020). Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning. In Anne L. Marte, lPurang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. 199-208).
Schaffert, R., Wang, J., Fischer, P., Borsdorf, A., & Maier, A. (2020). Learning-based misalignment detection for 2-D/3-D overlays. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 230-235). Berlin, DE: Springer.
Schaffert, R., Weiß, M., Wang, J., Borsdorf, A., & Maier, A. (2020). Learning-based correspondence estimation for 2-D/3-D registration. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik aktuell (pp. 222-228). Berlin, DEU: Springer.
Schirrmacher, F., Lorch, B., Stimpel, B., Köhler, T., & Riess, C. (2020). SR²: Super-Resolution With Structure-Aware Reconstruction. In Proceedings of the 2020 IEEE International Conference on Image Processing (ICIP) (pp. 533-537). Online.
Schuller, B., Batliner, A., Bergler, C., Messner, E.-M., Hamilton, A., Amiriparian, S.,... Hantke, S. (2020). The INTERSPEECH 2020 Computational Paralinguistics Challenge: Elderly Emotion, Breathing & Masks. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 (pp. 2042-2046). Shanghai, China: International Speech Communication Association.
Sindel, A., Maier, A., & Christlein, V. (2020). A Visualization Tool for Image Fusion of Artworks. In Proceedings of the 25th International Conference on Cultural Heritage and New Technologies. Online (Vienna, Austria).
Tolxdorff, T., Deserno, T., Maier, A., Handels, H., Maier-Hein, K., & Palm, C. (2020). Vorwort. In Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm (Eds.), Informatik Aktuell. (pp. XI-XII). Springer.
Weichselbaumer, N., Seuret, M., Limbach, S., Hinrichsen, L., Maier, A., & Christlein, V. (2020, February). The rapid rise of Fraktur. Paper presentation at 7. Tagung des Verbands Digital Humanities im deutschsprachigen Raum, Paderborn, DE.
Our research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.
Research projects
Font Group Recognition for Improved OCR
(Third Party Funds Single)
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)
Although OCR-D made huge progress in the last project phase in providing OCR for early printed books, it still faces two major problems: The huge variety of the material makes it extremely challenging to use generic OCR-models. Yet, selecting specific models is not possible as the sheer amount of material prevents a fully automatic workflow. This situation is further complicated by the lack of appropriate OCR training data. Current data sets consist overwhelmingly of texts in Fraktur, especially from the 19th century. This completely neglects the large typographic variety displayed by printing in the three previous centuries. Therefore, and in response to the demand from SLUB Dresden and ULB Halle, we propose to improve the current situation significantly1) fine tuning our font group recognition system to such a degree that it can be used at character level;2) transcribing more specific OCR training data for the 16th-18th century, which includes popular fonts such as Schwabacher, other bastards and old Fraktur styles; 3) training font-specific OCR models as well as integrated models that recognise both typeface and text simultaneously. This approach has ensured in other contexts that the network performs better on both individual tasks, as we can thus reduce overfitting during training. This project will improve OCR quality significantly, especially for books in non-Fraktur fonts. It will also provide a training data set of very high quality that can be reused in long term. Finally, the project will provide a more fine-grained font recognition tool that, beyond enabling font-specific OCR, also has important applications in text attribute recognition and layout analysis.
ODEUROPA: Negotiating Olfactory and Sensory Experiences in Cultural Heritage Practice and Research
(Third Party Funds Group – Sub project)
Term: 1. January 2021 - 31. December 2022
Funding source: EU - 8. Rahmenprogramm - Horizon 2020
URL: https://odeuropa.eu/
Our senses are gateways to the past. Although museums are slowly discovering the power of multi-sensory presentations, we lack the scientific standards, tools and data to identify, consolidate, and promote the wide-ranging role of scents and smelling in our cultural heritage. In recent years, European cultural heritage institutions have invested heavily in large-scale digitization. A wealth of object, text and image data that can be analysed using computer science techniques now exists. However, the potential olfactory descriptions, experiences, and memories that they contain remain unexplored. We recognize this as both a challenge and an opportunity. Odeuropa will apply state-of-the-art AI techniques to text and image datasets that span four centuries of European history. It will identify the vocabularies, spaces, events, practices, and emotions associated with smells and smelling. The project will curate this multi-modal information, following semantic web standards, and store the enriched data in a ‘European Olfactory Knowledge Graph’ (EOKG). We will use this data to identify ‘storylines’, informed by cultural history and heritage research, and share these with different audiences in different formats: through demonstrators, an online catalogue, toolkits and training documentation describing best-practices in olfactory museology. New, evidence-based methodologies will quantify the impact of multisensory visitor engagement. This data will support the implementation of policy recommendations for recognising, promoting, presenting and digitally preserving olfactory heritage. These activities will realize Odeuropa’s main goal: to show that smells and smelling are important and viable means for consolidating and promoting Europe’s tangible and intangible cultural heritage.
Intelligent MR Diagnosis of the Liver by Linking Model and Data-driven Processes (iDELIVER)
(Third Party Funds Single)
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
The project examines the use and further development of machine learning methods for MR image reconstruction and for the classification of liver lesions. Based on a comparison model and data-driven image reconstruction methods, these are to be systematically linked in order to enable high acceleration without sacrificing diagnostic value. In addition to the design of suitable networks, research should also be carried out to determine whether metadata (e.g. age of the patient) can be incorporated into the reconstruction. Furthermore, suitable classification algorithms on an image basis are to be developed and the potential of direct classification on the raw data is to be explored. In the long term, intelligent MR diagnostics can significantly increase the efficiency of use of MR hardware, guarantee better patient care and set new impulses in medical technology.
Bereitstellung einer Infrastruktur zur Nutzung für die Ausbildung Studierender auf einem z/OS Betriebssystem der Fa. IBM
(FAU Funds)
Funding source: Friedrich-Alexander-Universität Erlangen-Nürnberg
Molecular Assessment of Signatures ChAracterizing the Remission of Arthritis
(Third Party Funds Single)
Funding source: Bundesministerium für Bildung und Forschung (BMBF)
MASCARA zielt auf eine detaillierte, molekulare Charakterisierung der Remission bei Arthritis ab. Das Projekt basiert auf der kombinierten klinischen und technischen Erfahrung von Rheumatologen, Radiologen, Medizinphysikern, Nuklearmedizinern, Gastroenterologen, grundlagenwissenschaftlichen Biologen und Informatikern und verbindet fünf akademische Fachzentren in Deutschland. Das Projekt adressiert 1) den Umstand der zunehmenden Zahl von Arthritis Patienten in Remission, 2) die Herausforderungen, eine effektive Unterdrückung der Entzündung von einer Heilung zu unterscheiden und 3) das begrenzte Wissen über die Gewebeveränderungen in den Gelenken von Patienten mit Arthritis. MASCARA wird auf der Grundlage vorläufiger Daten vier wichtige mechanistische Bereiche (immunstoffwechselbedingte Veränderungen, mesenchymale Gewebereaktionen, residente Immunzellen und Schutzfunktion des Darms) untersuchen, die gemeinsam den molekularen Zustand der Remission bestimmen. Das Projekt zielt auf die Sammlung von Synovialbiopsien und die anschließende Gewebeanalyse bei Patienten mit aktiver Arthritis und Patienten in Remission ab. Die Gewebeanalysen umfassen (Einzelzell)-mRNA-Sequenzierung, Massenzytometrie sowie die Messung von Immunmetaboliten und werden durch molekulare Bildgebungsverfahren wie CEST-MRT und FAPI-PET ergänzt. Sämtliche Daten, die in dem Vorhaben generiert werden, werden in einem bereits bestehenden Datenbanksystem mit den Daten der anderen Partner zusammengeführt und gespeichert. Das Zusammenführen der Daten soll – mit Hilfe von maschinellem Lernen – krankheitsspezifische und mit der Krankheitsaktivität verbundene Mustermatrizen identifizieren.
Deep-Learning basierte Segmentierung und Landmarkendetektion auf Röntgenbildern für unfallchirurgische Eingriffe
(Third Party Funds Single)
Funding source: Siemens AG
Improving multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information
(Non-FAU Project)
This project aims to improve multi-modal quantitative SPECT with Deep Learning approaches to optimize image reconstruction and extraction of medical information. Such improvements include noise reduction and artifact removal from data acquired in SPECT.
Advancing osteoporosis medicine by observing bone microstructure and remodelling using a four-dimensional nanoscope
(Third Party Funds Single)
Funding source: European Research Council (ERC)
URL: https://cordis.europa.eu/project/id/810316
Due to Europe's ageing society, there has been a dramatic increase in the occurrence of osteoporosis (OP) and related diseases. Sufferers have an impaired quality of life, and there is a considerable cost to society associated with the consequent loss of productivity and injuries. The current understanding of this disease needs to be revolutionized, but study has been hampered by a lack of means to properly characterize bone structure, remodeling dynamics and vascular activity. This project, 4D nanoSCOPE, will develop tools and techniques to permit time-resolved imaging and characterization of bone in three spatial dimensions (both in vitro and in vivo), thereby permitting monitoring of bone remodeling and revolutionizing the understanding of bone morphology and its function.
To advance the field, in vivo high-resolution studies of living bone are essential, but existing techniques are not capable of this. By combining state-of-the art image processing software with innovative 'precision learning' software methods to compensate for artefacts (due e.g. to the subject breathing or twitching), and innovative X-ray microscope hardware which together will greatly speed up image acquisition (aim is a factor of 100), the project will enable in vivo X-ray microscopy studies of small animals (mice) for the first time. The time series of three-dimensional X-ray images will be complemented by correlative microscopy and spectroscopy techniques (with new software) to thoroughly characterize (serial) bone sections ex vivo.
The resulting three-dimensional datasets combining structure, chemical composition, transport velocities and local strength will be used by the PIs and international collaborators to study the dynamics of bone microstructure. This will be the first time that this has been possible in living creatures, enabling an assessment of the effects on bone of age, hormones, inflammation and treatment.
Deep Learning based Noise Reduction for Hearing Aids
(Third Party Funds Single)
Funding source: Industrie
Reduction of unwanted environmental noises is an important feature of today’s hearing aids, which is why noise reduction is nowadays included in almost every commercially available device. The majority of these algorithms, however, is restricted to the reduction of stationary noises. Due to the large number of different background noises in daily situations, it is hard to heuristically cover the complete solution space of noise reduction schemes. Deep learning-based algorithms pose a possible solution to this dilemma, however, they sometimes lack robustness and applicability in the strict context of hearing aids.
In this project we investigate several deep learning.based methods for noise reduction under the constraints of modern hearing aids. This involves a low latency processing as well as the employing a hearing instrument-grade filter bank. Another important aim is the robustness of the developed methods. Therefore, the methods will be applied to real-world noise signals recorded with hearing instruments.
Deep Learning Applied to Animal Linguistics
(FAU Funds)
For marine biologists, the interpretation and understanding of underwater audio recordings is essential. Based on such recordings, possible conclusions about behaviour, communication and social interactions of marine animals can be made. Despite a large number of biological studies on the subject of orca vocalizations, it is still difficult to recognize a structure or semantic/syntactic significance of orca signals in order to be able to derive any language and/or behavioral patterns. Due to a lack of techniques and computational tools, hundreds of hours of underwater recordings are still manually verified by marine biologists in order to detect potential orca vocalizations. In a post process these identified orca signals are analyzed and categorized. One of the main goals is to provide a robust and automatic method which is able to automatically detect orca calls within underwater audio recordings. A robust detection of orca signals is the baseline for any further and deeper analysis. Call type identification and classification based on pre-segmented signals can be used in order to derive semantic and syntactic patterns. In connection with the associated situational video recordings and behaviour descriptions (provided by several researchers on site) can provide potential information about communication (kind of a language model) and behaviors (e.g. hunting, socializing). Furthermore, orca signal detection can be used in conjunction with a localization software in order to provide researchers on the field with a more efficient way of searching the animals as well as individual recognition.
For more information about the DeepAL project please contact christian.bergler@fau.de.
2022
2021
2020
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