TY - JOUR A1 - Trepczynski, Adam A1 - Kneifel, Paul A1 - Heyland, Mark A1 - Leskovar, Marko A1 - Moewis, Philippe A1 - Damm, Philipp A1 - Taylor, William R. A1 - Zachow, Stefan A1 - Duda, Georg N. T1 - Impact of the external knee flexion moment on patello-femoral loading derived from in vivo loads and kinematics JF - Frontiers in Bioengineering and Biotechnology N2 - Introduction: Anterior knee pain and other patello-femoral (PF) complications frequently limit the success of total knee arthroplasty as the final treatment of end stage osteoarthritis. However, knowledge about the in-vivo loading conditions at the PF joint remains limited, as no direct measurements are available. We hypothesised that the external knee flexion moment (EFM) is highly predictive of the PF contact forces during activities with substantial flexion of the loaded knee. Materials and methods: Six patients (65–80 years, 67–101 kg) with total knee arthroplasty (TKA) performed two activities of daily living: sit-stand-sit and squat. Tibio-femoral (TF) contact forces were measured in vivo using instrumented tibial components, while synchronously internal TF and PF kinematics were captured with mobile fluoroscopy. The measurements were used to compute PF contact forces using patient specific musculoskeletal models. The relationship between the EFM and the PF contact force was quantified using linear regression. Results: Mean peak TF contact forces of 1.97–3.24 times body weight (BW) were found while peak PF forces reached 1.75 to 3.29 times body weight (BW). The peak EFM ranged from 3.2 to 5.9 %BW times body height, and was a good predictor of the PF contact force (R2 = 0.95 and 0.88 for sit-stand-sit and squat, respectively). Discussion: The novel combination of in vivo TF contact forces and internal patellar kinematics enabled a reliable assessment of PF contact forces. The results of the regression analysis suggest that PF forces can be estimated based solely on the EFM from quantitative gait analysis. Our study also demonstrates the relevance of PF contact forces, which reach magnitudes similar to TF forces during activities of daily living. Y1 - 2024 U6 - https://doi.org/10.3389/fbioe.2024.1473951 SN - 2296-4185 VL - 12/2024 PB - Frontiers Media SA ER - TY - JOUR A1 - Sengupta, Agniva A1 - Bartoli, Adrien T1 - Convex Solutions to SfT and NRSfM under Algebraic Deformation Models JF - IEEE Transactions on Pattern Analysis and Machine Intelligence N2 - We present nonlinear formulations to Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) faithfully exploiting the isometric, conformal and equiareal deformation models. Existing work uses relaxations such as inextensibility or requires knowing the optic flow field around the correspondences, an impractical assumption. In contrast, the proposed formulations only require point correspondences and resolve all ambiguities using the notions of maximal depth and maximal isometry heuristics. We propose solution methods using Semi-Definite Programming (SDP) for all formulations. We show that straightforward SDP models conflict with the usual maximal depth heuristic and propose an adapted opposite-depth parameterisation demonstrating a lesser relaxation gap. Experimental results on many real-world benchmark datasets demonstrate superior accuracy over existing methods. Y1 - 2025 U6 - https://doi.org/10.1109/TPAMI.2025.3635039 ER - TY - CHAP A1 - Manogue, Kevin A1 - Schang, Tomasz A1 - Kuş, Dilara A1 - Müller, Jonas A1 - Zachow, Stefan A1 - Sengupta, Agniva T1 - Generalizing Shape-from-Template to Topological Changes T2 - Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference N2 - Reconstructing the surfaces of deformable objects from correspondences between a 3D template and a 2D image is well studied under Shape-from-Template (SfT) methods; however, existing approaches break down when topological changes accompany the deformation. We propose a principled extension of SfT that enables reconstruction in the presence of such changes. Our approach is initialized with a classical SfT solution and iteratively adapts the template by partitioning its spatial domain so as to minimize an energy functional that jointly encodes physical plausibility and reprojection consistency. We demonstrate that the method robustly captures a wide range of practically relevant topological events including tears and cuts on bounded 2D surfaces, thereby establishing the first general framework for topological-change-aware SfT. Experiments on both synthetic and real data confirm that our approach consistently outperforms baseline methods. Y1 - 2025 SN - 978-3-03868-296-7 U6 - https://doi.org/10.2312/stag.20251322 PB - The Eurographics Association ER - TY - JOUR A1 - Schwope, A.D. A1 - Marsh, T.R. A1 - Parsons, S.G. A1 - Vogel, J. A1 - Dhillon, V.S. T1 - Unveiling the white dwarf in the eclipsing polar HU Aquarii JF - Astronomy & Astrophysics Y1 - 2025 U6 - https://doi.org/10.1051/0004-6361/202557306 ER - TY - JOUR A1 - Li, Jianning A1 - Bitter, Kerstin A1 - Nguyen, Anh Duc A1 - Shemesh, Hagay A1 - Zaslansky, Paul A1 - Zachow, Stefan T1 - Computational Insights into Root Canal Treatment: A Survey of Selected Methods in Imaging, Segmentation, Morphological Analysis, and Clinical Management JF - dentistry journal N2 - Background/Objectives: Root canal treatment (RCT) is a common dental procedure performed to preserve teeth by removing infected or at-risk pulp tissue caused by caries, trauma, or other pulpal conditions. A successful outcome, among others, depends on accurate identification of the root canal anatomy, planning a suitable therapeutic strategy, and ensuring a bacteria-tight root canal filling. Despite advances in dental techniques, there remains limited integration of computational methods to support key stages of treatment. This review aims to provide a comprehensive overview of computational methods applied throughout the full workflow of RCT, examining their potential to support clinical decision-making, improve treatment planning and outcome assessment, and help bridge the interdisciplinary gap between dentistry and computational research. Methods: A comprehensive literature review was conducted to identify and analyze computational methods applied to different stages of RCT, including root canal segmentation, morphological analysis, treatment planning, quality evaluation, follow-up, and prognosis prediction. In addition, a taxonomy based on application was developed to categorize these methods based on their function within the treatment process. Insights from the authors’ own research experience were also incorporated to highlight implementation challenges and practical considerations. Results: The review identified a wide range of computational methods aimed at enhancing the consistency and efficiency of RCT. Key findings include the use of advanced image processing for segmentation, image analysis for diagnosis and treatment planning, machine learning for morphological classification, and predictive modeling for outcome estimation. While some methods demonstrate high sensitivity and specificity in diagnostic and planning tasks, many remain in experimental stages and lack clinical integration. There is also a noticeable absence of advanced computational techniques for micro-computed tomography and morphological analysis. Conclusions: Computational methods offer significant potential to improve decision-making and outcomes in RCT. However, greater focus on clinical translation and development of cross-modality methodology is needed. The proposed taxonomy provides a structured framework for organizing existing methods and identifying future research directions tailored to specific phases of treatment. This review serves as a resource for both dental professionals, computer scientists and researchers seeking to bridge the gap between clinical practice and computational innovation. Y1 - 2025 U6 - https://doi.org/https://doi.org/10.3390/dj13120579 VL - 13 IS - 12 PB - MDPI ER - TY - JOUR A1 - Pusch, Larissa A1 - Conrad, Tim T1 - Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Biomedical Question Answering JF - BioMedInformatics N2 - Advancements in natural language processing (NLP), particularly Large Language Models (LLMs), have greatly improved how we access knowledge. However, in critical domains like biomedicine, challenges like hallucinations—where language models generate infor- mation not grounded in data—can lead to dangerous misinformation. This paper presents a hybrid approach that combines LLMs with Knowledge Graphs (KGs) to improve the accuracy and reliability of question-answering systems in the biomedical field. Our method, implemented using the LangChain framework, includes a query-checking algorithm that checks and, where possible, corrects LLM-generated Cypher queries, which are then exe- cuted on the Knowledge Graph, grounding answers in the KG and reducing hallucinations in the evaluated cases. We evaluated several LLMs, including several GPT models and Llama 3.3:70b, on a custom benchmark dataset of 50 biomedical questions. GPT-4 Turbo achieved 90% query accuracy, outperforming most other models. We also evaluated prompt engineering, but found little statistically significant improvement compared to the standard prompt, except for Llama 3:70b, which improved with few-shot prompting. To enhance usability, we developed a web-based interface that allows users to input natural language queries, view generated and corrected Cypher queries, and inspect results for accuracy. This framework improves reliability and accessibility by accepting natural language questions and returning verifiable answers directly from the knowledge graph, enabling inspection and reproducibility. The source code for generating the results of this paper and for the user- interface can be found in our Git repository: https://git.zib.de/lpusch/cyphergenkg-gui, accessed on 1 November 2025. Y1 - 2025 U6 - https://doi.org/10.3390/biomedinformatics5040070 VL - 5 ER - TY - JOUR A1 - Maier, Kristina A1 - Weiser, Martin A1 - Conrad, Tim T1 - Hybrid PDE-ODE Models for Efficient Simulation of Infection Spread in Epidemiology JF - Proceedings of the Royal Society A N2 - This paper introduces a novel hybrid model combining Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs) to simulate infectious disease dynamics across geographic regions. By leveraging the spatial detail of PDEs and the computational efficiency of ODEs, the model enables rapid evaluation of public health interventions. Applied to synthetic environments and real-world scenarios in Lombardy, Italy, and Berlin, Germany, the model highlights how interactions between PDE and ODE regions affect infection dynamics, especially in high-density areas. Key findings reveal that the placement of model boundaries in densely populated regions can lead to inaccuracies in infection spread, suggesting that boundaries should be positioned in areas of lower population density to better reflect transmission dynamics. Additionally, regions with low population density hinder infection flow, indicating a need for incorporating, e.g., jumps in the model to enhance its predictive capabilities. Results indicate that the hybrid model achieves a balance between computational speed and accuracy, making it a valuable tool for policymakers in real-time decision-making and scenario analysis in epidemiology and potentially in other fields requiring similar modeling approaches. Y1 - 2025 U6 - https://doi.org/10.1098/rspa.2024.0421 VL - 481 IS - 2306 PB - Royal Society ER - TY - CHAP A1 - Navayazdani, Esfandiar T1 - Ridge Regression for Manifold-valued Time-Series with Application to Meteorological Forecast T2 - Geometric Science of Information N2 - We propose a natural intrinsic extension of the ridge regression from Euclidean spaces to general manifolds, which relies on Riemannian least-squares fitting, empirical covariance, and Mahalanobis distance. We utilize it for time-series prediction and apply the approach to forecast hurricane tracks and their wind speeds. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-032-03921-7_1 SP - 3 EP - 11 PB - Springer Nature ER - TY - JOUR A1 - Lützkendorf, Janine A1 - Matkovic-Rachid, Tanja A1 - Liu, Sunbin A1 - Götz, Torsten A1 - Gao, Lili A1 - Turrel, Oriane A1 - Maglione, Marta A1 - Grieger, Melanie A1 - Putignano, Sabrina A1 - Ramesh, Niraja A1 - Ghelani, Tina A1 - Neumann, Alexander A1 - Gimber, Niclas A1 - Schmoranzer, Jan A1 - Stawrakakis, Anastasia A1 - Brence, Blaž A1 - Baum, Daniel A1 - Ludwig, Kai A1 - Heine, Martin A1 - Mielke, Thorsten A1 - Liu, Fan A1 - Walter, Alexander A1 - Wahl, Markus A1 - Sigrist, Stephan T1 - Blobby is a synaptic active zone assembly protein required for memory in Drosophila JF - Nature Communications Y1 - 2025 U6 - https://doi.org/10.1038/s41467-024-55382-9 VL - 16 ER - TY - JOUR A1 - Leskovar, Marko A1 - Heyland, Mark A1 - Trepczynski, Adam A1 - Zachow, Stefan T1 - Comparison of Global and Local Optimization Methods for Intensity-based 2D-3D Registration JF - Computers in Biology and Medicine Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2024.109574 VL - 186 SP - 109574 PB - Elsevier ER - TY - JOUR A1 - Schindler, M A1 - Flaum, B A1 - Surapaneni, VA A1 - Hayat, K A1 - Wölfer, J A1 - Baum, D A1 - Hu, R A1 - Kong, TF A1 - Doube, M A1 - Dean, MN T1 - Helmeted hornbill cranial kinesis: balancing mobility and stability in a high-impact joint JF - The Anatomical Record Y1 - 2025 U6 - https://doi.org/10.1002/ar.25613 ER - TY - JOUR A1 - Surapaneni, VA A1 - Flaum, B A1 - Schindler, M A1 - Hayat, K A1 - Wölfer, J A1 - Baum, D A1 - Hu, R A1 - Kong, TF A1 - Doube, M A1 - Dean, MN T1 - The helmeted hornbill casque is reinforced by a bundle of exceptionally thick, rod-like trabeculae JF - Annals of the New York Academy of Sciences Y1 - 2025 U6 - https://doi.org/10.1111/nyas.15254 VL - 1544 IS - 1 SP - 78 EP - 91 ER - TY - CHAP A1 - Siqueira Rodrigues, Lucas A1 - Kosch, Thomas A1 - Nyakatura, John A1 - Zachow, Stefan A1 - Israel, Johann Habakuk T1 - MorphoHaptics: An Open-Source Tool for Visuohaptic Exploration of Morphological Image Datasets T2 - KUI '24: Proceedings of the 21th International Conference on Culture and Computer Science: from Humanism to Digital Humanities N2 - Although digital methods have significantly advanced morphology, practitioners are still challenged to understand and process tomographic data of specimens. As automated processing of fossil data is still insufficient, morphologists still engage in intensive manual work to digitally prepare fossils for research objectives. We present an open-source tool that enables morphologists to explore tomographic data similarly to the physical workflows that traditional fossil preparators experience in the field. Using questionnaires, we assessed the usability of our prototype for virtual fossil preparation and related common tasks in the digital preparation workflow. Our findings indicate that integrating haptics into the virtual preparation workflow enhances the understanding of the morphology and material properties of working specimens and that the visuohaptic sculpting of fossil volumes is straightforward and is an improvement over current digital specimen processing methods. Y1 - 2025 U6 - https://doi.org/10.1145/3719236.3719271 SP - 1 EP - 10 ER - TY - CHAP A1 - Klenert, Nicolas A1 - Schwoerer, Finn A1 - Hajarolasvadi, Noushin A1 - Bournez, Siloé A1 - Arlt, Tobias A1 - Mahnke, Heinz-Eberhard A1 - Lepper, Verena A1 - Baum, Daniel T1 - Improving the Identification of Layers in 3D Images of Ancient Papyrus using Artificial Neural Networks T2 - 2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW), Tucson, AZ, USA N2 - The process of digitally unfolding ancient documents, such as folded papyrus packages, from 3D image data aims to be a non-invasive means to make previously hidden writing visible without risking to damage the precious documents. One of the main tasks necessary to digitally unfold a document is the geometric reconstruction of the writing substrate, which is a prerequisite for its subsequent unfolding. All current reconstruction methods require the existence of an interspace between different layers of the document to ensure a correct topology. Layers that appear merged together in the 3D image often result in wrong connections between layers and thus also in a wrong topology of the reconstructed geometry, which hinders the successful unfolding. Here, we propose to use a neural network to facilitate the discrimination of the layers. Using papyrus documents as an example of a particularly difficult writing material, we show that this significantly reduces the number of wrong connections and improves the overall identification of the layers. This in turn enables fully automatic digital unfolding of large areas of highly complex papyrus packages. Utilizing explainable AI (XAI) further allows us to explore the results of the applied neural network. Y1 - 2025 U6 - https://doi.org/10.1109/WACVW65960.2025.00143 SP - 1204 EP - 1212 ER - TY - JOUR A1 - Jain, Toshit A1 - Singh, Upkar A1 - Singh, Varun A1 - Boda, Vijay Kumar A1 - Hotz, Ingrid A1 - Vadhiyar, Sathish S. A1 - Vinayachandran, P. N. A1 - Natarajan, Vijay T1 - A Scalable System for Visual Analysis of Ocean Data JF - Computer Graphics Forum Y1 - 2025 U6 - https://doi.org/10.1111/cgf.15279 VL - 44 IS - 1 SP - e15279 ER - TY - JOUR A1 - Boulanger-Weill, Jonathan A1 - Kaempf, Florian A1 - L. Schalek, Richard A1 - Petkova, Mariela A1 - Vohra, Sumit Kumar A1 - Savaliya, Jay H. A1 - Wu, Yuelong A1 - Schuhknecht, Gregor F. P. A1 - Naumann, Heike A1 - Eberle, Maren A1 - Kirchberger, Kim N. A1 - Rencken, Simone A1 - Bianco, Isaac H. A1 - Baum, Daniel A1 - Bene, Filippo Del A1 - Engert, Florian A1 - Lichtman, Jeff W. A1 - Bahl, Armin T1 - Correlative light and electron microscopy reveals the fine circuit structure underlying evidence accumulation in larval zebrafish JF - bioRxiv N2 - Accumulating information is a critical component of most circuit computations in the brain across species, yet its precise implementation at the synaptic level remains poorly understood. Dissecting such neural circuits in vertebrates requires precise knowledge of functional neural properties and the ability to directly correlate neural dynamics with the underlying wiring diagram in the same animal. Here we combine functional calcium imaging with ultrastructural circuit reconstruction, using a visual motion accumulation paradigm in larval zebrafish. Using connectomic analyses of functionally identified cells and computational modeling, we show that bilateral inhibition, disinhibition, and recurrent connectivity are prominent motifs for sensory accumulation within the anterior hindbrain. We also demonstrate that similar insights about the structure-function relationship within this circuit can be obtained through complementary methods involving cell-specific morphological labeling via photo-conversion of functionally identified neuronal response types. We used our unique ground truth datasets to train and test a novel classifier algorithm, allowing us to assign functional labels to neurons from morphological libraries where functional information is lacking. The resulting feature-rich library of neuronal identities and connectomes enabled us to constrain a biophysically realistic network model of the anterior hindbrain that can reproduce observed neuronal dynamics and make testable predictions for future experiments. Our work exemplifies the power of hypothesis-driven electron microscopy paired with functional recordings to gain mechanistic insights into signal processing and provides a framework for dissecting neural computations across vertebrates. Y1 - 2025 U6 - https://doi.org/10.1101/2025.03.14.643363 ER - TY - JOUR A1 - Li, Tairan A1 - Schindler, Mike A1 - Paskin, Martha A1 - Surapaneni, Venkata A. A1 - Scott, Elliott A1 - Hauert, Sabine A1 - Payne, Nicholas A1 - Cade, David E. A1 - Goldbogen, Jeremy A. A1 - Mollen, Frederik H. A1 - Baum, Daniel A1 - Hanna, Sean A1 - Dean, Mason N. T1 - Functional models from limited data: a parametric and multimodal approach to anatomy and 3D kinematics of feeding in basking sharks (Cetorhinus maximus) JF - The Anatomical Record Y1 - 2025 U6 - https://doi.org/10.1002/ar.25693 ER - TY - JOUR A1 - Anteghini, Marco A1 - Gualdi, Francesco A1 - Oliva, Baldo T1 - How did we get there? AI applications to biological networks and sequences JF - Computers in Biology and Medicine N2 - The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein–protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field. Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2025.110064 SN - 0010-4825 VL - 190 PB - Elsevier BV ER - TY - JOUR A1 - Bostanci, Inan A1 - Conrad, Tim T1 - Integrating Agent-Based and Compartmental Models for Infectious Disease Modeling: A Novel Hybrid Approach JF - Journal of Artificial Societies and Social Simulation N2 - This study investigates the spatial integration of agent-based models (ABMs) and compartmental models for infectious disease modeling, presenting a novel hybrid approach and examining its implications. ABMs offer detailed insights by simulating interactions and decisions among individuals but are computationally expensive for large populations. Compartmental models capture population-level dynamics more efficiently but lack granular detail. We developed a hybrid model that aims to balance the granularity of ABMs with the computational efficiency of compartmental models, offering a more nuanced understanding of disease spread in diverse scenarios, including large populations. This model spatially couples discrete and continuous populations by integrating an ordinary differential equation model with a spatially explicit ABM. Our key objectives were to systematically assess the consistency of disease dynamics and the computational efficiency across various configurations. For this, we evaluated two experimental scenarios and varied the influence of each sub-model via spatial distribution. In the first, the ABM component modeled a homogeneous population; in the second, it simulated a heterogeneous population with landscape-driven movement. Results show that the hybrid model can significantly reduce computational costs but is sensitive to between-model differences, highlighting the importance of model equivalence in hybrid approaches. The code is available at: git.zib.de/ibostanc/hybrid_abm_ode. Y1 - 2025 U6 - https://doi.org/10.18564/jasss.5567 VL - 28 IS - 1 ER - TY - JOUR A1 - Sengupta, Agniva A1 - Zachow, Stefan T1 - Shape-from-Template with Generalised Camera JF - Image and Vision Computing N2 - This article presents a new method for non-rigidly registering a 3D shape to 2D keypoints observed by a constellation of multiple cameras. Non-rigid registration of a 3D shape to observed 2D keypoints, i.e., Shape-from-Template (SfT), has been widely studied using single images, but SfT with information from multiple-cameras jointly opens new directions for extending the scope of known use-cases such as 3D shape registration in medical imaging and registration from hand-held cameras, to name a few. We represent such multi-camera setup with the generalised camera model; therefore any collection of perspective or orthographic cameras observing any deforming object can be registered. We propose multiple approaches for such SfT: the first approach where the corresponded keypoints lie on a direction vector from a known 3D point in space, the second approach where the corresponded keypoints lie on a direction vector from an unknown 3D point in space but with known orientation w.r.t some local reference frame, and a third approach where, apart from correspondences, the silhouette of the imaged object is also known. Together, these form the first set of solutions to the SfT problem with generalised cameras. The key idea behind SfT with generalised camera is the improved reconstruction accuracy from estimating deformed shape while utilising the additional information from the mutual constraints between multiple views of a deformed object. The correspondence-based approaches are solved with convex programming while the silhouette-based approach is an iterative refinement of the results from the convex solutions. We demonstrate the accuracy of our proposed methods on many synthetic and real data. Y1 - 2025 U6 - https://doi.org/10.1016/j.imavis.2025.105579 VL - 162 ER - TY - JOUR A1 - Petkova, Mariela D. A1 - Januszewski, Michał A1 - Blakely, Tim A1 - Herrera, Kristian J. A1 - Schuhknecht, Gregor F.P. A1 - Tiller, Robert A1 - Choi, Jinhan A1 - Schalek, Richard L. A1 - Boulanger-Weill, Jonathan A1 - Peleg, Adi A1 - Wu, Yuelong A1 - Wang, Shuohong A1 - Troidl, Jakob A1 - Vohra, Sumit Kumar A1 - Wei, Donglai A1 - Lin, Zudi A1 - Bahl, Armin A1 - Tapia, Juan Carlos A1 - Iyer, Nirmala A1 - Miller, Zachary T. A1 - Hebert, Kathryn B. A1 - Pavarino, Elisa C. A1 - Taylor, Milo A1 - Deng, Zixuan A1 - Stingl, Moritz A1 - Hockling, Dana A1 - Hebling, Alina A1 - Wang, Ruohong C. A1 - Zhang, Lauren L. A1 - Dvorak, Sam A1 - Faik, Zainab A1 - King, Jr., Kareem I. A1 - Goel, Pallavi A1 - Wagner-Carena, Julian A1 - Aley, David A1 - Chalyshkan, Selimzhan A1 - Contreas, Dominick A1 - Li, Xiong A1 - Muthukumar, Akila V. A1 - Vernaglia, Marina S. A1 - Carrasco, Teodoro Tapia A1 - Melnychuck, Sofia A1 - Yan, TingTing A1 - Dalal, Ananya A1 - DiMartino, James A1 - Brown, Sam A1 - Safo-Mensa, Nana A1 - Greenberg, Ethan A1 - Cook, Michael A1 - Finley, Samantha A1 - Flynn, Miriam A. A1 - Hopkins, Gary Patrick A1 - Kovalyak, Julie A1 - Leonard, Meghan A1 - Lohff, Alanna A1 - Ordish, Christopher A1 - Scott, Ashley L. A1 - Takemura, Satoko A1 - Smith, Claire A1 - Walsh, John J. A1 - Berger, Daniel R. A1 - Pfister, Hanspeter A1 - Berg, Stuart A1 - Knecht, Christopher A1 - Meissner, Geoffrey W. A1 - Korff, Wyatt A1 - Ahrens, Misha B A1 - Jain, Viren A1 - Lichtman, Jeff W. A1 - Engert, Florian T1 - A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain JF - bioRxiv N2 - We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the dataset’s utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain. Y1 - 2025 U6 - https://doi.org/10.1101/2025.06.10.658982 ER - TY - JOUR A1 - Deppe, Dominik A1 - Gabriele, Matteo A1 - Mazzoleni, Manuel Giovanni A1 - Ordas-Bayon, Alejandro A1 - Fidanza, Andrea A1 - Rozhko, Yuriy A1 - Şimşek, Ekin Kaya A1 - Keltz, Eran A1 - Osterhoff, Georg A1 - Damm, Philipp A1 - Duda, Georg N. A1 - Leskovar, Marko A1 - Zachow, Stefan A1 - Trepczynski, Adam A1 - Heyland, Mark T1 - Interobserver Reliability of the Modified Radiographic Union Score (mRUST) for Tibial and Femoral Fractures JF - Journal of Orthopaedic Trauma N2 - OBJECTIVES: To evaluate the reliability of the modified Radiographic Union Score for Tibial fractures (mRUST) as a reliable tool for monitoring lower limb fractures (femur, tibia) treated with various modalities (nail, plate). METHODS: Design: Retrospective analysis. Setting: Single center academic hospital in Germany. Patient Selection Criteria: Adult patients (≥18 years) with extra-articular long bone fractures of the lower extremities treated surgically between January 2005 and April 2022, requiring radiographs in two perpendicular planes and at least one follow-up visit, were included. Exclusion criteria were critical clinical conditions, inability to consent, joint articulation fractures, inadequate documentation, or insufficient imaging quality. Outcome Measures and Comparisons: Six international investigators (five orthopedic surgeons, one radiologist) independently assessed fracture line and callus growth per cortex (mRUST) at individualized follow-up time points based on clinical practice. To evaluate interrater reliability, intraclass correlation coefficients were calculated for the overall dataset, and for subsets of rated images, that were defined based on anatomical location (femur/tibia), treatment type (plate/nail fixation), and treatment combinations across locations. RESULTS: A total of 166 patients (63 femur fractures, 103 tibia fractures; 32.5% female, mean age 43.4 (18–84)) with 1136 follow-up time points were analyzed. Overall interrater reliability for mRUST was good (intraclass correlation coefficient 0.77), consistent across fixation methods (nail/plate fixation, 0.79) and anatomical locations (tibia, 0.78; femur, 0.81). Cortex-specific reliability varied, with highest agreement for the medial cortex (0.70–0.74) and lowest for the posterior cortex (0.65–0.74). CONCLUSIONS: The mRUST (radiographic score) demonstrated reliability for monitoring fracture healing in the femur and tibia, irrespective of fixation method, supporting its use as a generalizable tool across lower limb fractures. LEVEL OF EVIDENCE: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence. Y1 - 2025 U6 - https://doi.org/10.1097/BOT.0000000000003032 SN - 0890-5339 VL - 39 IS - 10 SP - 557 EP - 563 ER - TY - CHAP A1 - Brence, Blaž A1 - Fuchs, Joachim A1 - Hiesinger, Peter Robin A1 - Baum, Daniel ED - Garrison, Laura ED - Krueger, Robert T1 - Fully automated quantification of synaptic locations in multi-channel Drosophila photoreceptor microscopy data T2 - Eurographics Workshop on Visual Computing for Biology and Medicine N2 - The workload posed by image analysis remains a major bottleneck for advances across the life sciences. To address this challenge, we have developed a fully automated workflow for processing complex 3D multi-channel microscopy images. Specifically, our workflow addresses the analysis of photoreceptor synapses in confocal images of the Drosophila melanogaster optic lobe. The workflow consists of multiple stages, combining traditional and machine learning–based approaches for image analysis and visual computing. It performs segmentation of brain regions, photoreceptor instance identification, and precise localization of synapses. The key novelty of the workflow is an automatic alignment of synapses into a cylindrical reference coordinate system, enabling comparative synaptic analysis across photoreceptors. To demonstrate the workflow’s applicability, preliminary biological results and their interpretation based on 50 images are presented. While the workflow is still being improved further, here, we showcase its capacity for efficient and objective data processing for high-throughput neurobiological analyses. Y1 - 2025 U6 - https://doi.org/10.2312/vcbm.20251254 ER - TY - JOUR A1 - Weimann, Kuba A1 - Conrad, Tim T1 - Self-supervised pre-training with joint-embedding predictive architecture boosts ECG classification performance JF - Computers in Biology and Medicine N2 - Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated datasets, which are difficult and costly to collect. To address this issue, transfer learning is often employed, where models are pre-trained on large datasets and fine-tuned for specific ECG classification tasks with limited labeled data. Self-supervised learning has become a widely adopted pre-training method, enabling models to learn meaningful representations from unlabeled datasets. In this work, we explore the joint-embedding predictive architecture (JEPA) for self-supervised learning from ECG data. Unlike invariance-based methods, JEPA does not rely on hand-crafted data augmentations, and unlike generative methods, it predicts latent features rather than reconstructing input data. We create a large unsupervised pre-training dataset by combining ten public ECG databases, amounting to over one million records. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks. Our results show that JEPA outperforms existing invariance-based and generative approaches, achieving an AUC of 0.945 on the PTB-XL all statements task. JEPA consistently learns the highest quality representations, as demonstrated in frozen evaluations, and proves advantageous for pre-training even in the absence of additional data. Y1 - 2025 U6 - https://doi.org/10.1016/j.compbiomed.2025.110809 SN - 0010-4825 VL - 196 PB - Elsevier BV ER - TY - JOUR A1 - Vohra, Sumit Kumar A1 - Eberle, Maren A1 - Boulanger-Weill, Jonathan A1 - Petkova, Mariela D. A1 - Schuhknecht, Gregor F. P. A1 - Herrera, Kristian J. A1 - Kämpf, Florian A1 - Ruetten, Virginia M. S. A1 - Lichtman, Jeff W. A1 - Engert, Florian A1 - Randlett, Owen A1 - Bahl, Armin A1 - Isoe, Yasuko A1 - Hege, Hans-Christian A1 - Baum, Daniel T1 - Fishexplorer: A multimodal cellular atlas platform for neuronal circuit dissection in larval zebrafish JF - bioRxiv N2 - Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community. Y1 - 2025 U6 - https://doi.org/10.1101/2025.07.14.664689 ER - TY - JOUR A1 - Shewarega, Michael A1 - Troidl, Jakob A1 - Alvarado Rodriguez, Oliver A1 - Dindoost, Mohammad A1 - Harth, Philipp A1 - Haberkern, Hannah A1 - Stegmaier, Johannes A1 - Bader, David A1 - Pfister, Hanspeter T1 - MoMo - Combining Neuron Morphology and Connectivity for Interactive Motif Analysis in Connectomes JF - IEEE Transactions on Visualization and Computer Graphics N2 - Connectomics, a subfield of neuroscience, reconstructs structural and functional brain maps at synapse-level resolution. These complex spatial maps consist of tree-like neurons interconnected by synapses. Motif analysis is a widely used method for identifying recurring subgraph patterns in connectomes. These motifs, thus, potentially represent fundamental units of information processing. However, existing computational tools often oversimplify neurons as mere nodes in a graph, disregarding their intricate morphologies. In this paper, we introduce MoMo, a novel interactive visualization framework for analyzing neuron morphology-aware motifs in large connectome graphs. First, we propose an advanced graph data structure that integrates both neuronal morphology and synaptic connectivity. This enables highly efficient, parallel subgraph isomorphism searches, allowing for interactive morphological motif queries. Second, we develop a sketch-based interface that facilitates the intuitive exploration of morphology-based motifs within our new data structure. Users can conduct interactive motif searches on state-of-the-art connectomes and visualize results as interactive 3D renderings. We present a detailed goal and task analysis for motif exploration in connectomes, incorporating neuron morphology. Finally, we evaluate MoMo through case studies with four domain experts, who asses the tool’s usefulness and effectiveness in motif exploration, and relevance to real-world neuroscience research. The source code for MoMo is available here: https://github.com/VCG/momo. Y1 - 2025 U6 - https://doi.org/10.1109/TVCG.2025.3634808 ER - TY - CHAP A1 - Maignant, Elodie A1 - Conrad, Tim A1 - von Tycowicz, Christoph T1 - Tree inference with varifold distances T2 - Geometric Science of Information. GSI 2025 N2 - In this paper, we consider a tree inference problem motivated by the critical problem in single-cell genomics of reconstructing dynamic cellular processes from sequencing data. In particular, given a population of cells sampled from such a process, we are interested in the problem of ordering the cells according to their progression in the process. This is known as trajectory inference. If the process is differentiation, this amounts to reconstructing the corresponding differentiation tree. One way of doing this in practice is to estimate the shortest-path distance between nodes based on cell similarities observed in sequencing data. Recent sequencing techniques make it possible to measure two types of data: gene expression levels, and RNA velocity, a vector that predicts changes in gene expression. The data then consist of a discrete vector field on a (subset of a) Euclidean space of dimension equal to the number of genes under consideration. By integrating this velocity field, we trace the evolution of gene expression levels in each single cell from some initial stage to its current stage. Eventually, we assume that we have a faithful embedding of the differentiation tree in a Euclidean space, but which we only observe through the curves representing the paths from the root to the nodes. Using varifold distances between such curves, we define a similarity measure between nodes which we prove approximates the shortest-path distance in a tree that is isomorphic to the target tree. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-032-03921-7_30 VL - 16034 ER - TY - JOUR A1 - Coomber, Celvic A1 - Chewle, Surahit A1 - Secker, Christopher A1 - Fackeldey, Konstantin A1 - Weber, Marcus A1 - Winkelmann, Stefanie A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Investigating Endogenous Opioids Unravels the Mechanisms Behind Opioid-Induced Constipation, a Mathematical Modeling Approach JF - International Journal of Molecular Sciences N2 - Endogenous opioids, such as Endomorphin-2, are not typically associated with severe constipation, unlike pharmaceutical opioids, which induce opioid-induced constipation (OIC) by activating μ-opioid receptors in the gastrointestinal tract. In this study, we present a mathematical model, which integrates the serotonergic and opioid pathways, simulating the interaction between serotonin and opioid signaling within the enteric nervous system (ENS). The model explores the mechanisms underlying OIC, with a focus on the change in adenylyl cyclase (AC) activity, cAMP accumulation, and the distinct functionalities of Endomorphin-2 compared to commonly used pharmaceutical opioids. We study the effects of Morphine, Fentanyl, and Methadone and contrast them with Endomorphin-2. Our findings reveal that opioids do not perturb the signaling of serotonin, but only the activity of AC, suggesting that serotonin levels have no influence on improving opioid-induced constipation. Furthermore, this study reveals that the primary difference between endogenous and pharmaceutical opioids is their degradation rates. This finding shows that modulating opioid degradation rates significantly improves cAMP recovery. In conclusion, our insights steer towards exploring opioid degrading enzymes, localized to the gut, as a strategy for mitigating OIC. Y1 - 2025 U6 - https://doi.org/10.3390/ijms26136207 VL - 26 IS - 13 ER - TY - JOUR A1 - Bartoli, Adrien A1 - Sengupta, Agniva T1 - Camera Pose in SfT and NRSfM under Isometric and Weaker Deformation Models JF - Computer Vision and Image Understanding N2 - Camera pose is a very natural concept in 3D vision in the rigid setting. It is however much more difficult to work with in deformable settings. Consequently, numerous deformable reconstruction methods simply ignore camera pose. We analyse the concept of pose in deformable settings and prove that it is unconstrained with the existing formulations, properly justifying the existing pose-less methods reconstructing structure only. We explain this result intuitively by the impossibility to define an intrinsic coordinate frame to a general deforming object. The proposed analysis uses the isometric deformation model and extends to the weaker models including conformality and equiareality. We propose a novel prior to rescue camera pose estimation in deformable settings, which attributes the deforming object’s dominant rigid-body motion to the camera. We show that adding this prior to any existing formulation fully constrains camera pose and leads to elegant two-step solution methods, involving deformable structure reconstruction using a base method in the first step, and absolute orientation or Procrustes analysis in the second step. We derive the proposed approach for the template-based and template-less settings, respectively implemented using Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) as base methods, and validate them experimentally, showing that the computed pose is qualitatively and quantitatively plausible. Y1 - 2025 U6 - https://doi.org/10.1016/j.cviu.2025.104488 VL - 261 ER - TY - THES A1 - Amiranashvili, Tamaz T1 - Universal and Expressive Statistical Shape Models for Anatomical Structures N2 - Form and function of anatomical structures are intimately linked. Pathological changes in form can be associated with the loss of function. For example, diseases often cause characteristic shape changes, making shape a sensitive structural biomarker for medical diagnosis. If the link between form and function is causal, correcting a pathological shape can even restore the healthy function of an organ. Accurate shape reconstruction is then crucial for effective, patient-specific treatment planning. This demonstrates the importance of shape in clinical interventions and its potential to improve overall patient outcomes. Statistical shape models are computational methods that capture shape variations in a given population and enable precise shape analysis and generation. We focus on two key properties of a good statistical shape model. First, it should be easy to construct, and second, it should accurately represent the underlying shape distribution. Established existing approaches can only be constructed from surfaces with pre-defined dense correspondence. Such correspondence is tedious to obtain, can introduce undesired biases, and prevents training on partial or sparse observations. While correspondence-free methods exist, they struggle to accurately capture shape distributions with intricate details and large variations. In this thesis, we develop shape models that simplify training and improve accuracy over state-of-the-art. To achieve these goals, we build on approximately diffeomorphic neural deformations and implicit neural representations. First, our proposed methods are trainable on correspondence-free surfaces and even partial segmentations with large slice distances. This makes them universal since they can be trained on heterogeneous data, enabling scalability to large datasets and avoiding potential biases of pre-defined correspondence. Second, our methods are highly expressive, accurately capturing intricate shape details in complex distributions. We evaluate effectiveness of our models on multiple anatomical structures, outperforming established baselines in both generative and discriminative settings. Y1 - 2025 UR - https://mediatum.ub.tum.de/?id=1776778 UR - https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20250717-1776778-0-4 ER - TY - JOUR A1 - Xie, Kunpeng A1 - Gruber, Lennart Johannes A1 - Crampen, Martin A1 - Li, Yao A1 - Ferreira, André A1 - Tappeiner, Elias A1 - Gillot, Maxime A1 - Schepers, Jan A1 - Xu, Jiangchang A1 - Pankert, Tobias A1 - Beyer, Michel A1 - Shahamiri, Negar A1 - ten Brink, Reinier A1 - Dot, Gauthier A1 - Weschke, Charlotte A1 - van Nistelrooij, Niels A1 - Verhelst, Pieter-Jan A1 - Guo, Yan A1 - Xu, Zhibin A1 - Bienzeisler, Jonas A1 - Rashad, Ashkan A1 - Flügge, Tabea A1 - Cotton, Ross A1 - Vinayahalingam, Shankeeth A1 - Ilesan, Robert A1 - Raith, Stefan A1 - Madsen, Dennis A1 - Seibold, Constantin A1 - Xi, Tong A1 - Bergé, Stefaan A1 - Nebelung, Sven A1 - Kodym, Oldřich A1 - Sundqvist, Osku A1 - Thieringer, Florian A1 - Lamecker, Hans A1 - Coppens, Antoine A1 - Potrusil, Thomas A1 - Kraeima, Joep A1 - Witjes, Max A1 - Wu, Guomin A1 - Chen, Xiaojun A1 - Lambrechts, Adriaan A1 - Cevidanes, Lucia H Soares A1 - Zachow, Stefan A1 - Hermans, Alexander A1 - Truhn, Daniel A1 - Alves, Victor A1 - Egger, Jan A1 - Röhrig, Rainer A1 - Hölzle, Frank A1 - Puladi, Behrus T1 - Beyond Benchmarks: Towards Robust Artificial Intelligence Bone Segmentation in Socio-Technical Systems JF - Expert Systems With Applications N2 - Despite the advances in automated medical image segmentation, AI models still underperform in various clinical settings, challenging real-world integration. In this multicenter evaluation, we analyzed 20 state-of-the-art mandibular segmentation models across 19,218 segmentations of 1,000 clinically resampled CT/CBCT scans. We show that segmentation accuracy varies by up to 25% depending on socio-technical factors such as voxel size, bone orientation, and patient conditions such as osteosynthesis or pathology. Higher sharpness, isotropic smaller voxels, and neutral orientation significantly improved results, while metallic osteosynthesis and anatomical complexity led to significant degradation. Our findings challenge the common view of AI models as “plug-and-play” tools and suggest evidence-based optimization recommendations for both clinicians and developers. This will in turn boost the integration of AI segmentation tools in routine healthcare. Y1 - 2025 UR - https://www.medrxiv.org/content/10.1101/2025.06.11.25329022v1 U6 - https://doi.org/10.1016/j.eswa.2025.130031 VL - 299 IS - Part D ER - TY - JOUR A1 - Okafornta, Chukwuebuka William A1 - Farhadifar, Reza A1 - Fabig, Gunar A1 - Wu, Hai-Yin A1 - Köckert, Maria A1 - Vogel, Martin A1 - Baum, Daniel A1 - Haase, Robert A1 - Shelley, Michael J. A1 - Needleman, Daniel J. A1 - Müller-Reichert, Thomas T1 - Cell size reduction scales spindle elongation but not chromosome segregation in C. elegans JF - bioRxiv N2 - How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2– to 64–cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation with large-scale electron tomography, we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo, spindle-length scaling across nematode species spanning ~100 million years of divergence, and spindle rotation in human cells. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species. Y1 - 2025 U6 - https://doi.org/10.1101/2025.10.13.681585 ER - TY - JOUR A1 - Hu, Chenhao A1 - Tutika, Ravi A1 - Deng, Zhifei A1 - Jia, Zian A1 - Chen, Liuni A1 - Chen, Hongshun A1 - Geng, Yang A1 - Xiao, Xianghui A1 - Shevchenko, Pavel D. A1 - Pierre, Christoph A1 - Weaver, James C. A1 - Baum, Daniel A1 - Bartlett, Michael D. A1 - Li, Ling T1 - Mineralized sclerites in the gorgonian coral Leptogorgia chilensis as a natural jamming system JF - PNAS Y1 - 2025 U6 - https://doi.org/10.1073/pnas.2504541122 VL - 122 IS - 44 ER - TY - JOUR A1 - Sterzik, Anna A1 - Krone, Michael A1 - Baum, Daniel A1 - Cunningham, Douglas W. A1 - Lawonn, Kai T1 - Uncertainty Visualization for Biomolecular Structures: An Empirical Evaluation JF - IEEE Transactions on Visualization and Computer Graphics N2 - Uncertainty is an intrinsic property of almost all data, regardless of the data being measured, simulated, or generated. It can significantly influence the results and reliability of subsequent analysis steps. Clearly communicating uncertainties is crucial for informed decision-making and understanding, especially in biomolecular data, where uncertainty is often difficult to infer. Uncertainty visualization (UV) is a powerful tool for this purpose. However, previously proposed UV methods lack sufficient empirical evaluation. We collected and categorized visualization methods for portraying positional uncertainty in biomolecular structures. We then organized the methods into metaphorical groups and extracted nine representatives: color, clouds, ensemble, hulls, sausages, contours, texture, waves, and noise. We assessed their strengths and weaknesses in a twofold approach: expert assessments with six domain experts and three perceptual evaluations involving 1,756 participants. Through the expert assessments, we aimed to highlight the advantages and limitations of the individual methods for the application domain and discussed areas for necessary improvements. Through the perceptual evaluation, we investigated whether the visualizations are intuitively associated with uncertainty and whether the directionality of the mapping is perceived as intended. We also assessed the accuracy of inferring uncertainty values from the visualizations. Based on our results, we judged the appropriateness of the metaphors for encoding uncertainty and suggest further areas for improvement. Y1 - 2025 U6 - https://doi.org/10.1109/TVCG.2025.3596385 VL - 31 IS - 12 SP - 10296 EP - 10310 ER - TY - JOUR A1 - Schulze, Kenrick A1 - Peppert, Felix A1 - Schütte, Christof A1 - Sunkara, Vikram T1 - Chimeric U-Net – Modifying the standard U-Net towards Explainability JF - Artificial Intelligence N2 - Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learning-based segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net , encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable AI. Code to build and utilize the Chimeric U-Net is made available under: https://github.com/kenrickschulze/Chimeric-UNet---Half-invertible-UNet-in-Pytorch Y1 - 2025 U6 - https://doi.org/10.1016/j.artint.2024.104240 VL - 338 ER - TY - CHAP A1 - Schade, Johannes A1 - von Tycowicz, Christoph A1 - Hanik, Martin T1 - Bi-invariant Geodesic Regression with Data from the Osteoarthritis Initiative T2 - Information Processing in Medical Imaging N2 - Many phenomena are naturally characterized by measuring continuous transformations such as shape changes in medicine or articulated systems in robotics. Modeling the variability in such datasets requires performing statistics on Lie groups, that is, manifolds carrying an additional group structure. As the Lie group captures the symmetries in the data, it is essential from a theoretical and practical perspective to ask for statistical methods that respect these symmetries; this way they are insensitive to confounding effects, e.g., due to the choice of reference coordinate systems. In this work, we investigate geodesic regression---a generalization of linear regression originally derived for Riemannian manifolds. While Lie groups can be endowed with Riemannian metrics, these are generally incompatible with the group structure. We develop a non-metric estimator using an affine connection setting. It captures geodesic relationships respecting the symmetries given by left and right translations. For its computation, we propose an efficient fixed point algorithm requiring simple differential expressions that can be calculated through automatic differentiation. We perform experiments on a synthetic example and evaluate our method on an open-access, clinical dataset studying knee joint configurations under the progression of osteoarthritis. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-96628-6_4 SP - 49 EP - 63 PB - Springer CY - Lecture Notes in Computer Science ER - TY - JOUR A1 - Brence, Blaž A1 - Brummer, Josephine A1 - Dercksen, Vincent J. A1 - Özel, Mehmet Neset A1 - Kulkarni, Abhishkek A1 - Wolterhoff, Neele A1 - Prohaska, Steffen A1 - Hiesinger, Peter Robin A1 - Baum, Daniel T1 - Semi-automatic Geometrical Reconstruction and Analysis of Filopodia Dynamics in 4D Two-Photon Microscopy Images JF - bioRxiv N2 - Background: Filopodia are thin and dynamic membrane protrusions that play a crucial role in cell migration, axon guidance, and other processes where cells explore and interact with their surroundings. Historically, filopodial dynamics have been studied in great detail in 2D in cultured cells, and more recently in 3D culture as well as living brains. However, there is a lack of efficient tools to trace and track filopodia in 4D images of complex brain cells. Results: To address this issue, we have developed a semi-automatic workflow for tracing filopodia in 3D images and tracking the traced filopodia over time. The workflow was developed based on high-resolution data of photoreceptor axon terminals in the in vivo context of normal Drosophila brain development, but devised to be applicable to filopodia in any system, including at different temporal and spatial scales. In contrast to the pre-existing methods, our workflow relies solely on the original intensity images without the requirement for segmentation or complex preprocessing. The workflow was realized in C++ within the Amira software system and consists of two main parts, dataset pre-processing, and geometrical filopodia reconstruction, where each of the two parts comprises multiple steps. In this paper, we provide an extensive workflow description and demonstrate its versatility for two different axo-dendritic morphologies, R7 and Dm8 cells. Finally, we provide an analysis of the time requirements for user input and data processing. Conclusion: To facilitate simple application within Amira or other frameworks, we share the source code, which is available athttps://github.com/zibamira/filopodia-tool. Y1 - 2025 U6 - https://doi.org/10.1101/2025.05.20.654789 ER - TY - JOUR A1 - Brence, Blaž A1 - Wandelt, Laura R. A1 - Walter, Sophie A1 - Sigrist, Stephan J. A1 - Petzoldt, Astrid G. A1 - Baum, Daniel T1 - Semi-automatic 3D-quantification of in-vivo synapse formation JF - ResearchSquare N2 - Background: Synapses, as specialised cell-cell contacts, allow for a faithful and controlled signal transmission between a neuron and a target cell. Presynapses, the sites of neurotransmitter release, form de novo throughout the development of an organism. Although this process is fundamental to the development and function of synaptic circuits, how developing neurons control number and distribution of individual synapses remains poorly understood. In-vivo imaging analysis of synapse formation at the neuromuscular junction of anaesthetised Drosophila third instar larvae allows for spatial and temporal resolution of the underlying molecular processes. However, high-throughput, comprehensive analysis are hampered by the manual and time-consuming imaging analysis methods applied hitherto. Here, we focus on the early presynaptic formation steps, that is, the presynaptic seeding, initiated by the formation of transient Liprin-a/SYD1 seeding sites, either stabilised or disintegrated over a time span of 30-90 min. Results: To investigate the dynamics of the Liprin-a/SYD1 seeding sites, we developed an automated analysis pipeline for 3D confocal images from in-vivo imaging at distinct time points to analyse fluorescently labelled presynaptic protein dynamics during early synapse formation. The workflow is realised in the data analysis software Amira, utilising the hierarchical watershed algorithm, and was designed for automatic processing with an option for manual proofreading. Compared to the previous 2D manual quantification, this automated approach provides a higher sensitivity in single Liprin-a seeding site detection in low-intensity areas and in regions of dense seeding sites.In addition, it substantially reduces the work time. To account for possible errors occurring in the automated processing, we implemented an additional proofreading step allowing for a manual correction of Liprin-a seeding site segmentation and assignment, thus greatly improving the analysis while only marginally increasing work time by 10% to a total work time reduction of 80% compared to the 2D manual analysis paradigm. Conclusion: The process of synaptogenesis underlies the general principles of locomotion, learning and memory formation. The developed fast and accurate semi-automated 3D workflow provides a substantial progress in the analysis of this molecular process and its application can be easily extended to other dynamic in-vivo research approaches across species. Y1 - 2025 U6 - https://doi.org/10.21203/rs.3.rs-6073150/v1 ER - TY - JOUR A1 - Eigen, Lennart A1 - Ladenburger, Pius A1 - Brence, Blaž A1 - Shubitidze, Ani A1 - Baum, Daniel A1 - Hildebrandt, Thomas A1 - Brecht, Michael T1 - Elephant trunk tip musculature reflects species differences in grasping behavior JF - Communications Biology N2 - Elephants use their trunks, muscular hydrostats, to perform a plethora of tasks. Trunk tip morphology as well as grasping behavior differ between elephant species. While African savanna elephants (Loxodonta africana) use their dorsal and ventral finger for pinching movements, Asian elephants (Elephas maximus) prefer to wrap around objects with their one dorsal finger and ventral bulb trunk tip lip. Moreover, E. maximus can flip their ventral bulb backwards to clamp objects behind the trunk tip. Whether trunk tip musculature differs between elephant species and muscle architecture is reflected by preferred grasping behavior is, however, not clear. In this study, we performed dense muscle fascicle reconstruction of three L. africana and three E. maximus hemi-trunk tips using a combination of manual and automated segmentation of high-resolution microfocus tomography (microCT) scans. We distinguish three types of muscle fascicles: longitudinal (bending and shortening), radial (elongating) and transversal muscle fascicles (elongating). We found that trunk tips of L. africana consist to one third of longitudinal and two thirds radial/transversal muscle fascicles, likely aiding in their grasping behavior, while E. maximus trunk tips consist to two thirds of longitudinal and one third radial/transversal muscle fascicles, which is advantageous for their wrapping and backward clamping behavior. Y1 - 2025 U6 - https://doi.org/10.1038/s42003-025-08998-6 VL - 8 ER - TY - JOUR A1 - Schmitt, Kira A1 - Titschack, Jürgen A1 - Baum, Daniel T1 - CoDA: Interactive Segmentation and Morphological Analysis of Dendroid Structures Exemplified on Stony Cold-Water Corals JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2026 U6 - https://doi.org/10.1109/TVCG.2026.3656066 ER - TY - CHAP A1 - Stokke, Jo Andersson A1 - Bergmann, Ronny A1 - Hanik, Martin A1 - von Tycowicz, Christoph T1 - p-Laplacians for Manifold-valued Hypergraphs T2 - Geometric Science of Information. GSI 2025 N2 - Hypergraphs extend traditional graphs by enabling the representation of N-ary relationships through higher-order edges. Akin to a common approach of deriving graph Laplacians, we define function spaces and corresponding symmetric products on the nodes and edges to derive hypergraph Laplacians. While this has been done before for Euclidean features, this work generalizes previous hypergraph Laplacian approaches to accommodate manifold-valued hypergraphs for many commonly encountered manifolds. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-032-03924-8_17 VL - 16035 ER -