TY - JOUR A1 - Toulkeridou, Evropi A1 - Gutierrez, Carlos Enrique A1 - Baum, Daniel A1 - Doya, Kenji A1 - Economo, Evan P. T1 - Automated segmentation of insect anatomy from micro-CT images using deep learning JF - bioRxiv Y1 - 2021 U6 - https://doi.org/10.1101/2021.05.29.446283 ER - TY - JOUR A1 - Lindow, Norbert A1 - Brünig, Florian A1 - Dercksen, Vincent J. A1 - Fabig, Gunar A1 - Kiewisz, Robert A1 - Redemann, Stefanie A1 - Müller-Reichert, Thomas A1 - Prohaska, Steffen A1 - Baum, Daniel T1 - Semi-automatic stitching of filamentous structures in image stacks from serial-section electron tomography JF - Journal of Microscopy N2 - We present a software-assisted workflow for the alignment and matching of filamentous structures across a three-dimensional (3D) stack of serial images. This is achieved by combining automatic methods, visual validation, and interactive correction. After the computation of an initial automatic matching, the user can continuously improve the result by interactively correcting landmarks or matches of filaments. Supported by a visual quality assessment of regions that have been already inspected, this allows a trade-off between quality and manual labor. The software tool was developed in an interdisciplinary collaboration between computer scientists and cell biologists to investigate cell division by quantitative 3D analysis of microtubules (MTs) in both mitotic and meiotic spindles. For this, each spindle is cut into a series of semi-thick physical sections, of which electron tomograms are acquired. The serial tomograms are then stitched and non-rigidly aligned to allow tracing and connecting of MTs across tomogram boundaries. In practice, automatic stitching alone provides only an incomplete solution, because large physical distortions and a low signal-to-noise ratio often cause experimental difficulties. To derive 3D models of spindles despite dealing with imperfect data related to sample preparation and subsequent data collection, semi-automatic validation and correction is required to remove stitching mistakes. However, due to the large number of MTs in spindles (up to 30k) and their resulting dense spatial arrangement, a naive inspection of each MT is too time-consuming. Furthermore, an interactive visualization of the full image stack is hampered by the size of the data (up to 100 GB). Here, we present a specialized, interactive, semi-automatic solution that considers all requirements for large-scale stitching of filamentous structures in serial-section image stacks. To the best of our knowledge, it is the only currently available tool which is able to process data of the type and size presented here. The key to our solution is a careful design of the visualization and interaction tools for each processing step to guarantee real-time response, and an optimized workflow that efficiently guides the user through datasets. The final solution presented here is the result of an iterative process with tight feedback loops between the involved computer scientists and cell biologists. Y1 - 2021 U6 - https://doi.org/10.1111/jmi.13039 VL - 284 IS - 1 SP - 25 EP - 44 ER - TY - JOUR A1 - Toulkeridou, Evropi A1 - Gutierrez, Carlos Enrique A1 - Baum, Daniel A1 - Doya, Kenji A1 - Economo, Evan P. T1 - Automated segmentation of insect anatomy from micro-CT images using deep learning JF - Natural Sciences N2 - Three-dimensional (3D) imaging, such as micro-computed tomography (micro-CT), is increasingly being used by organismal biologists for precise and comprehensive anatomical characterization. However, the segmentation of anatomical structures remains a bottleneck in research, often requiring tedious manual work. Here, we propose a pipeline for the fully-automated segmentation of anatomical structures in micro-CT images utilizing state-of-the-art deep learning methods, selecting the ant brain as a test case. We implemented the U-Net architecture for 2D image segmentation for our convolutional neural network (CNN), combined with pixel-island detection. For training and validation of the network, we assembled a dataset of semi-manually segmented brain images of 76 ant species. The trained network predicted the brain area in ant images fast and accurately; its performance tested on validation sets showed good agreement between the prediction and the target, scoring 80% Intersection over Union (IoU) and 90% Dice Coefficient (F1) accuracy. While manual segmentation usually takes many hours for each brain, the trained network takes only a few minutes. Furthermore, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests on distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggests that methods like the one presented here generally apply across diverse taxa. Our method makes the construction of segmented maps and the morphological quantification of different species more efficient and scalable to large datasets, a step toward a big data approach to organismal anatomy. Y1 - 2023 U6 - https://doi.org/10.1002/ntls.20230010 VL - 3 IS - 4 ER - TY - CHAP A1 - Harth, Philipp A1 - Bast, Arco A1 - Troidl, Jakob A1 - Meulemeester, Bjorge A1 - Pfister, Hanspeter A1 - Beyer, Johanna A1 - Oberlaender, Marcel A1 - Hege, Hans-Christian A1 - Baum, Daniel T1 - Rapid Prototyping for Coordinated Views of Multi-scale Spatial and Abstract Data: A Grammar-based Approach T2 - Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM) N2 - Visualization grammars are gaining popularity as they allow visualization specialists and experienced users to quickly create static and interactive views. Existing grammars, however, mostly focus on abstract views, ignoring three-dimensional (3D) views, which are very important in fields such as natural sciences. We propose a generalized interaction grammar for the problem of coordinating heterogeneous view types, such as standard charts (e.g., based on Vega-Lite) and 3D anatomical views. An important aspect of our web-based framework is that user interactions with data items at various levels of detail can be systematically integrated and used to control the overall layout of the application workspace. With the help of a concise JSON-based specification of the intended workflow, we can handle complex interactive visual analysis scenarios. This enables rapid prototyping and iterative refinement of the visual analysis tool in collaboration with domain experts. We illustrate the usefulness of our framework in two real-world case studies from the field of neuroscience. Since the logic of the presented grammar-based approach for handling interactions between heterogeneous web-based views is free of any application specifics, it can also serve as a template for applications beyond biological research. Y1 - 2023 U6 - https://doi.org/10.2312/vcbm.20231218 ER - TY - JOUR A1 - Longren, Luke L. A1 - Eigen, Lennart A1 - Shubitidze, Ani A1 - Lieschnegg, Oliver A1 - Baum, Daniel A1 - Nyakatura, John A. A1 - Hildebrandt, Thomas A1 - Brecht, Michael T1 - Dense Reconstruction of Elephant Trunk Musculature JF - Current Biology N2 - The elephant trunk operates as a muscular hydrostat and is actuated by the most complex musculature known in animals. Because the number of trunk muscles is unclear, we performed dense reconstructions of trunk muscle fascicles, elementary muscle units, from microCT scans of an Asian baby elephant trunk. Muscle architecture changes markedly across the trunk. Trunk tip and finger consist of about 8,000 extraordinarily filigree fascicles. The dexterous finger consists exclusively of microscopic radial fascicles pointing to a role of muscle miniaturization in elephant dexterity. Radial fascicles also predominate (at 82% volume) the remainder of the trunk tip and we wonder if radial muscle fascicles are of particular significance for fine motor control of the dexterous trunk tip. By volume, trunk-shaft muscles comprise one-third of the numerous, small radial muscle fascicles, two-thirds of the three subtypes of large longitudinal fascicles (dorsal longitudinals, ventral outer obliques, and ventral inner obliques), and a small fraction of transversal fascicles. Shaft musculature is laterally, but not radially, symmetric. A predominance of dorsal over ventral radial muscles and of ventral over dorsal longitudinal muscles may result in a larger ability of the shaft to extend dorsally than ventrally and to bend inward rather than outward. There are around 90,000 trunk muscle fascicles. While primate hand control is based on fine control of contraction by the convergence of many motor neurons on a small set of relatively large muscles, evolution of elephant grasping has led to thousands of microscopic fascicles, which probably outnumber facial motor neurons. Y1 - 2023 U6 - https://doi.org/10.1016/j.cub.2023.09.007 VL - 33 SP - 1 EP - 8 ER - TY - JOUR A1 - Kiewisz, Robert A1 - Baum, Daniel A1 - Müller-Reichert, Thomas A1 - Fabig, Gunar T1 - Serial-section electron tomography and quantitative analysis of the microtubule organization in 3D-reconstructed mitotic spindles JF - Bio-protocol Y1 - 2023 U6 - https://doi.org/10.21769/BioProtoc.4849 VL - 13 IS - 20 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 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 -