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 N2 - Dendroid stony corals build highly complex colonies that develop from a single coral polyp sitting in a cup-like skeleton, called corallite, by asexual reproduction, resulting in a tree-like branching pattern of its skeleton. Despite their beauty and ecological importance as reef builders in tropical shallow-water reefs as well as in cold-water coral mounds in the deep ocean, systematic studies investigating the ontogenetic morphological development of such coral colonies are largely missing. One reason for this is the sheer number of corallites – up to several thousands in a single coral colony. Another limiting factor, especially for the analysis of dendroid cold-water corals, is the existence of many secondary joints in the ideally tree-like structure that make a reconstruction of the skeleton tree extremely tedious. Herein, we present CoDA, the Coral Dendroid structure Analyzer, a visual analytics suite that allows for the first time to investigate the ontogenetic morphological development of complex dendroid coral colonies, exemplified on three important framework-forming dendroid cold-water corals: Lophelia pertusa (Linnaeus, 1758), Madrepora oculata (Linnaeus, 1758), and Goniocorella dumosa (Alcock, 1902). Input to CoDA is an initial instance segmentation of the coral polyp cavities (calices), from which it estimates the skeleton tree of the colony and extracts classical morphological measurements and advanced shape features of the individual corallites. CoDA also works as a proofreading and error correction tool by helping to identify wrong parts in the skeleton tree and providing tools to quickly correct these errors. The final skeleton tree enables the derivation of additional information about the calices/corallite instances that otherwise could not be obtained, including their ontogenetic generation and branching patterns – the basis of a fully quantitative statistical analysis of the coral colony morphology. Part of CoDA is CoDA.Graph, a feature-rich link-and-brush user interface for visualizing the extracted features and 2D graph layouts of the skeleton tree, enabling the real-time exploration of complex coral colonies and their building blocks, the individual corallites and branches. In the future, we expect CoDA to greatly facilitate the analysis of large stony corals of different species and morphotypes, as well as other dendroid structures, enabling new insights into the influence of genetic and environmental factors on their ontogenetic morphological development. Y1 - 2024 ER - TY - JOUR A1 - Vohra, Sumit Kumar A1 - Herrera, Kristian A1 - Tavhelidse-Suck, Tinatini A1 - Knoblich, Simon A1 - Seleit, Ali A1 - Boulanger-Weill, Jonathan A1 - Chambule, Sydney A1 - Aspiras, Ariel A1 - Santoriello, Cristina A1 - Randlett, Owen A1 - Wittbrodt, Joachim A1 - Aulehla, Alexander A1 - Lichtman, Jeff W. A1 - Fishman, Mark A1 - Hege, Hans-Christian A1 - Baum, Daniel A1 - Engert, Florian A1 - Isoe, Yasuko T1 - Multi-species community platform for comparative neuroscience in teleost fish JF - bioRxiv N2 - Studying neural mechanisms in complementary model organisms from different ecological niches in the same animal class can leverage the comparative brain analysis at the cellular level. To advance such a direction, we developed a unified brain atlas platform and specialized tools that allowed us to quantitatively compare neural structures in two teleost larvae, medaka (Oryzias latipes) and zebrafish (Danio rerio). Leveraging this quantitative approach we found that most brain regions are similar but some subpopulations are unique in each species. Specifically, we confirmed the existence of a clear dorsal pallial region in the telencephalon in medaka lacking in zebrafish. Further, our approach allows for extraction of differentially expressed genes in both species, and for quantitative comparison of neural activity at cellular resolution. The web-based and interactive nature of this atlas platform will facilitate the teleost community’s research and its easy extensibility will encourage contributions to its continuous expansion. Y1 - 2024 U6 - https://doi.org/10.1101/2024.02.14.580400 ER - TY - GEN A1 - Hajarolasvadi, Noushin A1 - Baum, Daniel T1 - Data for Training the DeepOrientation Model: Simulated cryo-ET tomogram patches N2 - A major restriction to applying deep learning methods in cryo-electron tomography is the lack of annotated data. Many large learning-based models cannot be applied to these images due to the lack of adequate experimental ground truth. One appealing alternative solution to the time-consuming and expensive experimental data acquisition and annotation is the generation of simulated cryo-ET images. In this context, we exploit a public cryo-ET simulator called PolNet to generate three datasets of two macromolecular structures, namely the ribosomal complex 4v4r and Thermoplasma acidophilum 20S proteasome, 3j9i. We select these two specific particles to test whether our models work for macromolecular structures with and without rotational symmetry. The three datasets contain 50, 150, and 450 tomograms with a voxel size of 10 ̊A, respectively. Here, we publish patches of size 40 × 40 × 40 extracted from the medium-sized dataset with 26,703 samples of 4v4r and 40,671 samples of 3j9i. The original tomograms from which the samples were extracted are of size 500 × 500 × 250. Finally, it should be noted that the currently published test dataset is employed for reporting the results of our paper titled ”DeepOrientation: Deep Orientation Estimation of Macromolecules in Cryo-electron tomography” paper. Y1 - 2024 U6 - https://doi.org/10.12752/9686 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 - Yang, Binru A1 - Knötel, David A1 - Ciecierska-Holmes, Jana A1 - Wölfer, Jan A1 - Chaumel, Júlia A1 - Zaslansky, Paul A1 - Baum, Daniel A1 - Fratzl, Peter A1 - Dean, Mason N. T1 - Growth of a tessellation: geometric rules for the development of stingray skeletal patterns JF - Advanced Science Y1 - 2024 U6 - https://doi.org/10.1002/advs.202407641 VL - 11 IS - 48 ER - TY - JOUR A1 - Mayer, Julius A1 - Baum, Daniel A1 - Ambellan, Felix A1 - von Tycowicz, Christoph A1 - for the Alzheimer’s Disease Neuroimaging Initiative, T1 - Shape-based Disease Grading via Functional Maps and Graph Convolutional Networks with Application to Alzheimer’s Disease JF - BMC Medical Imaging N2 - Shape analysis provides methods for understanding anatomical structures extracted from medical images. However, the underlying notions of shape spaces that are frequently employed come with strict assumptions prohibiting the analysis of incomplete and/or topologically varying shapes. This work aims to alleviate these limitations by adapting the concept of functional maps. Further, we present a graph-based learning approach for morphometric classification of disease states that uses novel shape descriptors based on this concept. We demonstrate the performance of the derived classifier on the open-access ADNI database differentiating normal controls and subjects with Alzheimer’s disease. Notably, the experiments show that our approach can improve over state-of-the-art from geometric deep learning. Y1 - 2024 U6 - https://doi.org/10.1186/s12880-024-01513-z VL - 24 ER - TY - CHAP A1 - Gossing, Anne A1 - Beckert, Andreas A1 - Fischer, Christoph A1 - Klenert, Nicolas A1 - Natarajan, Vijay A1 - Pacey, George A1 - Vogt, Thorwin A1 - Rautenhaus, Marc A1 - Baum, Daniel T1 - A Ridge-based Approach for Extraction and Visualization of 3D Atmospheric Fronts T2 - 2024 IEEE Visualization and Visual Analytics (VIS) N2 - An atmospheric front is an imaginary surface that separates two distinct air masses and is commonly defined as the warm-air side of a frontal zone with high gradients of atmospheric temperature and humidity. These fronts are a widely used conceptual model in meteorology, which are often encountered in the literature as two-dimensional (2D) front lines on surface analysis charts. This paper presents a method for computing three-dimensional (3D) atmospheric fronts as surfaces that is capable of extracting continuous and well-confined features suitable for 3D visual analysis, spatio-temporal tracking, and statistical analyses. Recently developed contour-based methods for 3D front extraction rely on computing the third derivative of a moist potential temperature field. Additionally, they require the field to be smoothed to obtain continuous large-scale structures. This paper demonstrates the feasibility of an alternative method to front extraction using ridge surface computation. The proposed method requires only the sec- ond derivative of the input field and produces accurate structures even from unsmoothed data. An application of the ridge-based method to a data set corresponding to Cyclone Friederike demonstrates its benefits and utility towards visual analysis of the full 3D structure of fronts. Y1 - 2024 U6 - https://doi.org/10.1109/VIS55277.2024.00043 SP - 176 EP - 180 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 - 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 - Sterzik, Anna A1 - Lichtenberg, Nils A1 - Krone, Michael A1 - Baum, Daniel A1 - Cunningham, Douglas W. A1 - Lawonn, Kai T1 - Enhancing molecular visualization: Perceptual evaluation of line variables with application to uncertainty visualization JF - Computers & Graphics N2 - Data are often subject to some degree of uncertainty, whether aleatory or epistemic. This applies both to experimental data acquired with sensors as well as to simulation data. Displaying these data and their uncertainty faithfully is crucial for gaining knowledge. Specifically, the effective communication of the uncertainty can influence the interpretation of the data and the user’s trust in the visualization. However, uncertainty-aware visualization has gotten little attention in molecular visualization. When using the established molecular representations, the physicochemical attributes of the molecular data usually already occupy the common visual channels like shape, size, and color. Consequently, to encode uncertainty information, we need to open up another channel by using feature lines. Even though various line variables have been proposed for uncertainty visualizations, they have so far been primarily used for two-dimensional data and there has been little perceptual evaluation. Thus, we conducted two perceptual studies to determine the suitability of the line variables blur, dashing, grayscale, sketchiness, and width for distinguishing several values in molecular visualizations. While our work was motivated by uncertainty visualization, our techniques and study results also apply to other types of scalar data. Y1 - 2023 U6 - https://doi.org/10.1016/j.cag.2023.06.006 VL - 114 SP - 401 EP - 413 ER -