MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909996969623552 |
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| author | Maga, A. Murat Fillion-Robin, Jean-Christophe |
| author_facet | Maga, A. Murat Fillion-Robin, Jean-Christophe |
| contents | The digitization of biological specimens has revolutionized the field of morphology, creating large collections of 3D data, and microCT in particular. This revolution was initially supported by the development of open-source software tools, specifically the development of SlicerMorph extension to the open-source image analytics platform 3D Slicer. Through SlicerMorph and 3D Slicer, biologists, morphologists and scientists in related fields have all the necessary tools to import, visualize and analyze these complex and large datasets in a single platform that is flexible and expandible, without the need of proprietary software that hinders scientific collaboration and sharing.
Yet, a significant "compute gap" remains: While data and software are now open and accessible, the necessary high-end computing resources to run them are often not equally accessible in all institutions, and particularly lacking at Primarily Undergraduate Institutions (PUIs) and other educational settings. Here, we present MorphoCloud, an "IssuesOps"-based platform that leverages Github Actions and the JetStream2 cloud farm to provide on-demand, research-grade computing environments to researchers working with 3D morphological datasets. By delivering a GPU-accelerated full desktop experience via a web browser, MorphoCloud eliminates hardware barriers, enabling complex 3D analysis and AI-assisted segmentation. This paper explains the platform and its architecture, as well as use cases it is designed to support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21408 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis Maga, A. Murat Fillion-Robin, Jean-Christophe Other Quantitative Biology The digitization of biological specimens has revolutionized the field of morphology, creating large collections of 3D data, and microCT in particular. This revolution was initially supported by the development of open-source software tools, specifically the development of SlicerMorph extension to the open-source image analytics platform 3D Slicer. Through SlicerMorph and 3D Slicer, biologists, morphologists and scientists in related fields have all the necessary tools to import, visualize and analyze these complex and large datasets in a single platform that is flexible and expandible, without the need of proprietary software that hinders scientific collaboration and sharing. Yet, a significant "compute gap" remains: While data and software are now open and accessible, the necessary high-end computing resources to run them are often not equally accessible in all institutions, and particularly lacking at Primarily Undergraduate Institutions (PUIs) and other educational settings. Here, we present MorphoCloud, an "IssuesOps"-based platform that leverages Github Actions and the JetStream2 cloud farm to provide on-demand, research-grade computing environments to researchers working with 3D morphological datasets. By delivering a GPU-accelerated full desktop experience via a web browser, MorphoCloud eliminates hardware barriers, enabling complex 3D analysis and AI-assisted segmentation. This paper explains the platform and its architecture, as well as use cases it is designed to support. |
| title | MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2512.21408 |