{"id":20956567,"url":"https://github.com/bluebrain/ultraliser","last_synced_at":"2025-05-14T05:32:13.679Z","repository":{"id":49850785,"uuid":"353361440","full_name":"BlueBrain/Ultraliser","owner":"BlueBrain","description":"Reconstruction of watertight meshes and annotated volumes of neuroscience spatial structures from non-watertight inputs, segmented masks, skeletons of NGV morphologies and volumes. ","archived":false,"fork":false,"pushed_at":"2024-04-11T20:53:09.000Z","size":9921,"stargazers_count":24,"open_issues_count":34,"forks_count":4,"subscribers_count":5,"default_branch":"master","last_synced_at":"2024-04-13T23:06:55.220Z","etag":null,"topics":["astrocyte","mesh","morphology","neuron","neurons","vasculature","volume"],"latest_commit_sha":null,"homepage":"https://portal.bluebrain.epfl.ch","language":"C++","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/BlueBrain.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":"AUTHORS.md","dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-03-31T13:10:59.000Z","updated_at":"2024-05-08T22:43:10.545Z","dependencies_parsed_at":"2023-10-05T01:36:06.281Z","dependency_job_id":"30aa4bcf-0a65-4ddf-ac85-cf3cc5cf126c","html_url":"https://github.com/BlueBrain/Ultraliser","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlueBrain%2FUltraliser","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlueBrain%2FUltraliser/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlueBrain%2FUltraliser/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BlueBrain%2FUltraliser/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BlueBrain","download_url":"https://codeload.github.com/BlueBrain/Ultraliser/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225277151,"owners_count":17448628,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["astrocyte","mesh","morphology","neuron","neurons","vasculature","volume"],"created_at":"2024-11-19T01:26:56.323Z","updated_at":"2024-11-19T01:26:57.030Z","avatar_url":"https://github.com/BlueBrain.png","language":"C++","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003cp align=\"center\"\u003e\n        \u003cimg src=\"docs/images/banner.jpg\" width=1200\u003e\n\u003c/p\u003e\n \n\n\n[![DOI](https://zenodo.org/badge/DOI/10.1093/bib.bbac491.svg)](https://doi.org/10.1093/bib/bbac491)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7105941.svg)](https://doi.org/10.5281/zenodo.7105941)\n\n\n# Ultraliser\n\n_Ultraliser_ is an __unconditionally robust__ and __high-performance__ framework dedicated primarily to **_in silico_ neuroscience** research. \n_Ultraliser_ is capable of generating high fidelity and multiscale 3D models (surface meshes and annotated volumes) of neuroscientific data, such as nuclei, mitochondria, endoplasmic reticula, \nneurons, astrocytes, pericytes, neuronal branches with dendritic spines, minicolumns with thousands of neurons and large networks of cerebral vasculature - with realistic geometries. \n\n_Ultraliser_ implements an effective __voxelization-based remeshing engine__ that can rasterize non-watertight surface meshes - in the \nform of __triangular soups__ - into high-resolution volumes, with which we can reconstruct topologically accurate, adaptively optimized, \nand watertight surface manifolds. \n\nIn addition to their importance for accurate quantitative analysis, the resulting models are primarily intended to automate the \nprocess of conducting supercomputer-based _in silico_ simulations of neuroscience experiments; complementing _in vivo_ and _in vitro_ \ntechniques. \n\nWatertight triangular meshes are used for (i) performing 3D particle simulations, (ii) mesh-based skeletonization, in which accurate morphologies of cellular structures are obtained for performing 1D compartmental simulations and (iii) tetrahedralization, in which we can generate tetrahedral volume meshes for 3D reaction-diffusion simulations.\nAnnotated volumetric tissue models are also used in _in silico_ imaging studies, where we can simulate optical imaging experiments with brightfield or fluorescence microscopy\u003csup\u003e10\u003c/sup\u003e. \n\n## Features \n\n+ Reconstruction of high fidelity, optimized\u003csup\u003e1\u003c/sup\u003e, and two-manifold watertight\u003csup\u003e2\u003c/sup\u003e triangular mesh models from non-watertight inputs represented by polygonal soups. \n+ Surface mesh smoothing and optimization using [Laplacian operators](https://en.wikipedia.org/wiki/Laplacian_smoothing) and feature-preserving adaptive mesh optimization\u003csup\u003e1\u003c/sup\u003e.\n+ Reconstruction of large-scale volumetric models\u003csup\u003e3\u003c/sup\u003e from non-watertight input meshes using high-performance [surface and solid voxelization](Mesh-Voxelization). \n+ Reconstruction of optimized and smooth surface meshes from input volumes using parallel implementations of the standard marching cubes\u003csup\u003e4\u003c/sup\u003e algorithm and the advanced dual marching cubes\u003csup\u003e5\u003c/sup\u003e algorithm. \n+ Reconstruction of optimized and smooth surface meshes from input binary masks of segmented data. \n+ Reconstruction of geometrically realistic watertight mesh models of spiny neurons from corresponding morphological skeletons\u003csup\u003e6\u003c/sup\u003e.\n+ Reconstruction of geometrically realistic watertight mesh models of complete astroglial cells\u003csup\u003e8\u003c/sup\u003e  (with endfeet) from input morphological skeletons and endfeet surface patches\u003csup\u003e9\u003c/sup\u003e.        \n+ Reconstruction of high-fidelity, optimized, and multi-partitioned vascular meshes from fragmented and large-scale vascular network graphs\u003csup\u003e7\u003c/sup\u003e. \n+ Morphology, mesh, and volume quantitative and qualitative analysis. \n+ Generation of color-coded multi-axis projections of spatial data (morphologies, meshes, and volumes) for visual analytics. \n\n## Documentation \n\nExhaustive user documentation, including step-by-step examples and detailed explanations of the \ncommand line options, is available on the [Wiki](https://github.com/BlueBrain/Ultraliser/wiki) of this repository.\n\n## Installation\n\nInstallation instructions are detailed on this [page on the Wiki](https://github.com/BlueBrain/Ultraliser/wiki/Installation). \n\n### Software Dependencies \n\n* [OpenMP](https://en.wikipedia.org/wiki/OpenMP), a multi-threading library for parallel processing on multi-core CPUs. \n* [libTIFF](http://www.libtiff.org/), which gives support for the Tag Image File Format (TIFF), a widely used format for storing image data.\n* [libhdf5](https://support.hdfgroup.org/HDF5/doc/cpplus_RM/index.html), or the Hierarchical Data Format 5 (HDF5) library for storing data.\n* [Eigen3](https://eigen.tuxfamily.org/), a template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.\n* [BZip2](https://www.sourceware.org/bzip2/), a high-quality data compressor.  \n* [ZLIB](https://docs.python.org/3/library/zlib.html), for data compression. \n* [FMT](https://github.com/fmtlib/fmt), a formatting library providing a fast and safe alternative to C stdio and C++ iostreams.\n* [GLM](https://github.com/g-truc/glm), a header-only C++ mathematics library for graphics software based on the OpenGL Shading Language (GLSL) specifications.\n\n### Supported Operating Systems \n\n_Ultraliser_ has been tested on Unix-based operating systems including:\n\n* Ubuntu 18.04, Ubuntu 20.04, Ubuntu 21.04, and Ubuntu 22.04.\n* RHEL7, RHEL8. \n* macOS 10.12 Sierra, 10.13 High Sierra,  10.14 Mojave, 10.15 Catalina.\n\n\n# Known Bugs or Feature Requests\n\nPlease refer to the [Github issue tracker](https://github.com/BlueBrain/Ultraliser/issues?utf8=%E2%9C%93\u0026q=) for fixed and open bugs. Users can also report any bugs and request new features needed for their research. We are happy to provide direct [support](#contact). \n\n# License \n\n_Ultraliser_ is available to download and use under the GNU General Public License, version 3 ([GPL](https://www.gnu.org/licenses/gpl.html), or “free software”). \nThe code is open-sourced with approval from the open-sourcing committee and principal coordinators of the Blue Brain Project in March 2021. \nSee the file [LICENSE](https://github.com/BlueBrain/Ultraliser/blob/master/LICENSE) for the full license.\n\n# Citation\n\nIf you use this software, kindly use the following ${\\mathrm{B{\\scriptstyle{IB}} T_{\\displaystyle E}X}}$ entry for citation:\n\n```\n@article{abdellah2023ultraliser,\n    author = {Abdellah, Marwan and Garc{\\'\\i}a Cantero, Juan Jos{\\'e} and Roman Guerrero, Nadir \n    and Foni, Alessandro and Coggan, Jay S. and Cal{\\`\\i}, Corrado and Agus, Marco and \n    Zisis, Eleftherios and Keller, Daniel and Hadwiger, Markus and Magistretti, Pierre and \n    Markram, Henry and Sch{\\\"u}rmann, Felix},\n    title = {Ultraliser: a framework for creating multiscale, high-fidelity and geometrically \n    realistic 3D models for in silico neuroscience},\n    journal = {Briefings in Bioinformatics},\n    volume={24},\n    number={1},\n    pages={bbac491},\n    year={2023},\n    publisher={Oxford University Press}\n}\n```\n\nThe initial revision of the manuscript was archived on bioRxiv\n```\n@article {abdellah2022.07.27.501675,\n    author = {Abdellah, Marwan and Garc{\\'\\i}a Cantero, Juan Jos{\\'e} and Roman Guerrero, Nadir \n    and Foni, Alessandro and Coggan, Jay S. and Cal{\\`\\i}, Corrado and Agus, Marco and \n    Zisis, Eleftherios and Keller, Daniel and Hadwiger, Markus and Magistretti, Pierre and \n    Markram, Henry and Sch{\\\"u}rmann, Felix},\n    title = {Ultraliser: a framework for creating multiscale, high-fidelity and geometrically \n    realistic 3D models for in silico neuroscience},\n    elocation-id = {2022.07.27.501675},\n    year = {2022},\n    doi = {10.1101/2022.07.27.501675},\n    publisher = {Cold Spring Harbor Laboratory},\n    URL = {https://www.biorxiv.org/content/early/2022/07/29/2022.07.27.501675},\n    journal = {bioRxiv}\n}\n```\n\n# Publications \n\nThe volume reconstruction algorithms in _Ultraliser_ are based on the following paper. \n\n```\n@article{abdellah2017reconstruction,\n  title={Reconstruction and visualization of large-scale volumetric models of neocortical \n  circuits for physically-plausible in silico optical studies},\n  author={Abdellah, Marwan and Hernando, Juan and Antille, Nicolas and Eilemann, Stefan and \n  Markram, Henry and Sch{\\\"u}rmann, Felix},\n  journal={BMC bioinformatics},\n  volume={18},\n  number={10},\n  pages={402},\n  year={2017},\n  publisher={BioMed Central}\n}\n```\n\n# Acknowledgement \u0026 Funding\n\nThe development of this software was supported by funding to the [Blue Brain Project](https://bluebrain.epfl.ch), a research center of the [École polytechnique fédérale de Lausanne (EPFL)](https://www.epfl.ch/), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.\nFinancial support was provided by competitive research funding from [King Abdullah University of Science and Technology (KAUST)](https://www.kaust.edu.sa/en).\n\n# Attributions\n\n* The volume reconstruction code is an extension to the work of [Marwan Abdellah's](http://marwan-abdellah.com/) [PhD (In silico Brain Imaging: Physically-plausible Methods for Visualizing Neocortical Microcircuitry)](https://infoscience.epfl.ch/record/232444?ln=en). \n\n* The mesh optimization code in _Ultraliser_ is based on the routines provided by the [GAMer (Geometry-preserving Adaptive MeshER) library](http://fetk.org/codes/gamer/). GAMer is developed by _Z. Yu, M. Holst, Y. Cheng, and J.A. McCammon_, and can be redistributed and modified under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License or any later version.\n\n* The watertighness verification code in _Ultraliser_ is based on an extended version of the [MeshFix library](https://github.com/MarcoAttene/MeshFix-V2.1). MeshFix is developed by _Marco Attene_, Consiglio Nazionale delle Ricerche, Istituto di Matematica Applicata e Tecnologie Informatiche, Sezione di Genova, IMATI-GE / CNR, and can be redistributed and modified under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License or any later version.    \n\n* The mesh analysis code is implemented based on the metrics described in [The Verdict Geometric Quality Library](https://coreform.com/papers/verdict_quality_library.pdf). \n\n* The morphology analysis code is implemented based on the metrics described in [NeuroMorphoVis](https://github.com/BlueBrain/NeuroMorphoVis/wiki/Analysis). \n\n* The values of the colormaps used to generate the color-coded projections are obtained from the [matplotlib](https://matplotlib.org/) library.\n\n* The H5 morphology samples are available with permission from the [Blue Brain Project, Ecole Polytechnique Federale de Lausanne (EPFL)](https://www.epfl.ch/research/domains/bluebrain/).\n\n* The SWC morphology samples of neurons and astrocytes are available from NeuroMorpho.Org. [NeuroMorpho.Org](neuromorpho.org) is a centrally curated inventory or repository of digitally reconstructed neurons associated with peer-reviewed publications.\n\n* The SWC morphology samples of brain vasculature are available from the [Brain Vasculature (BraVa) database](http://cng.gmu.edu/brava). \nThe Brain Vasculature (BraVa) database contains digital reconstructions of the human brain arterial \narborizations from 61 healthy adult subjects along with extracted morphological measurements.\nThe arterial arborizations include the six major trees stemming from the circle of Willis, \nnamely: the left and right Anterior Cerebral Arteries (ACAs), Middle Cerebral Arteries (MCAs), \nand Posterior Cerebral Arteries (PCAs). \nCitation: [Susan N. Wright, Peter Kochunov, Fernando Mut Maurizio Bergamino, Kerry M. Brown, John C. Mazziotta, \nArthur W. Toga, Juan R. Cebral, Giorgio A. Ascoli. \nDigital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic \nresonance angiography. NeuroImage, 82, 170-181, (2013)](http://dx.doi.org/10.1016/j.neuroimage.2013.05.089). \n\n* The VMV vascular morphologies are available with permission from [Pablo Blinder, Department of Neurobiology, Faculty of Life Sciences at Tel Aviv University](https://english.tau.ac.il/profile/pb). \n\n* The H5 vascular morphologies are available with permission from the \n[Blue Brain Project](https://bluebrain.epfl.ch/page-52063.html), \n[Ecole Polytechnique Federale de Lausanne (EPFL)](https://www.epfl.ch/). The original dataset \ncourtesy of [Bruno Weber](https://www.neuroscience.uzh.ch/en/about/people/steering/Weber.html), \nUniversity of Zurich, Switzerland.\n\n* Cortical meshes (in H5 format) are courtesy of the [MICrONS Consortium](https://www.microns-explorer.org/) including \n[Seung Lab](http://seunglab.org/), \n[Brain-map.org](https://brain-map.org) - [Allen Institute for Brain Science](https://alleninstitute.org/), \n[Tolias Lab](https://toliaslab.org/), and [IARPA Microns](https://www.iarpa.gov/index.php/research-programs/microns). \n    * The cortical mm^3 datasets are available from the following pulication:\n        * MICrONs Consortium et al. Functional connectomics spanning multiple areas of mouse visual cortex. bioRxiv 2021.07.28.454025; doi: https://doi.org/10.1101/2021.07.28.454025\n    * Layer 2/3 datasets are available from the following publications:\n        * Dorkenwald, S., Turner, N.L., Macrina, T., Lee, K., Lu, R., Wu, J., Bodor, A.L., Bleckert, A.A., Brittain, D., Kemnitz, N., et al. (2019). Binary and analog variation of synapses between cortical pyramidal neurons. bioRxiv 2019.12.29.890319; doi: https://doi.org/10.1101/2019.12.29.890319\n        * Schneider-Mizell, C. Bodor, A.L., Collman, F.  Brittain,D. Bleckert, AA, Dorkenwald, S., Turner N.L. Macrina, T.  Lee, K. Lu, R.  Wu, J. et al. (2020)  Chandelier cell anatomy and function suggest a variably distributed but common signal. bioRxiv 2020.03.31.018952v1; doi: https://doi.org/10.1101/2020.03.31.018952\n\n\n\nFull attributions and acknowledgements are available in the [ACKNOWLEDGEMENTS file](https://github.com/BlueBrain/Ultraliser/blob/master/ACKNOWLEDGEMENTS.md).\n\n# Contact\n\nFor more information on _Ultraliser_, comments, or suggestions, please contact:\n\n__Marwan Abdellah__  \nScientific Visualization Engineer  \nBlue Brain Project  \n[marwan.abdellah@epfl.ch](marwan.abdellah@epfl.ch) \n \n__Felix Schürmann__  \nCo-director of the Blue Brain Project    \n[felix.schuermann@epfl.ch](felix.schuermann@epfl.ch) \n\nShould you have any questions concerning press inquiries, please contact:\n\n__Evelyne Schmid__  \nCommunications  \nBlue Brain Project  \n[evelyne.schmidosborne@epfl.ch](evelyne.schmidosborne@epfl.ch)\n\n# References\n\n1. YU, Zeyun, HOLST, Michael J., CHENG, Yuhui, et al. Feature-preserving adaptive mesh generation for molecular shape modeling and simulation. Journal of Molecular Graphics and Modelling, 2008, vol. 26, no 8, p. 1370-1380.\n\n2. ATTENE, Marco. A lightweight approach to repairing digitized polygon meshes. The visual computer, 2010, vol. 26, no 11, p. 1393-1406.\n\n3. ABDELLAH, Marwan, HERNANDO, Juan, ANTILLE, Nicolas, et al. Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies. BMC bioinformatics, 2017, vol. 18, no 10, p. 39-50.\n\n4. LORENSEN, William E. et CLINE, Harvey E. Marching cubes: A high resolution 3D surface construction algorithm. ACM siggraph computer graphics, 1987, vol. 21, no 4, p. 163-169.\n\n5. NIELSON, Gregory M. Dual marching cubes. In : IEEE visualization 2004. IEEE, 2004. p. 489-496.\n\n6. ABDELLAH, Marwan, HERNANDO, Juan, EILEMANN, Stefan, et al. NeuroMorphoVis: a collaborative framework for analysis and visualization of neuronal morphology skeletons reconstructed from microscopy stacks. Bioinformatics, 2018, vol. 34, no 13, p. i574-i582.\n\n7. ABDELLAH, Marwan, GUERRERO, Nadir Román, LAPERE, Samuel, et al. Interactive visualization and analysis of morphological skeletons of brain vasculature networks with VessMorphoVis. Bioinformatics, 2020, vol. 36, no Supplement_1, p. i534-i541.\n\n8. ZISIS, Eleftherios, KELLER, Daniel, KANARI, Lida, et al. Digital reconstruction of the neuro-glia-vascular architecture. Cerebral Cortex, 2021, vol. 31, no 12, p. 5686-5703.\n\n9. ABDELLAH, Marwan, FONI, Alessandro, ZISIS, Eleftherios, et al. Metaball skinning of synthetic astroglial morphologies into realistic mesh models for in silico simulations and visual analytics. Bioinformatics, 2021, vol. 37, no Supplement_1, p. i426-i433.\n\n10. ABDELLAH, Marwan. In silico brain imaging: physically-plausible methods for visualizing neocortical microcircuitry. EPFL, 2017.\n\n--- \n\n\u003cp align=\"center\"\u003e\n        \u003cimg src=\"docs/images/logos/epfl-logo.png\" width=200\u003e\n\u003c/p\u003e\n \n\u003cp align=\"center\"\u003eCopyright (c) 2022 Blue Brain Project/EPFL\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbluebrain%2Fultraliser","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbluebrain%2Fultraliser","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbluebrain%2Fultraliser/lists"}