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Updated: Jul 5, 2026
From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
Published on: August 13, 2014
Biom3d, a modular framework to host and develop 3D segmentation methods
Guillaume Mougeot1, Sami Safarbati2, Hervé Alégot3
1UCA - Université Clermont Auvergne, CNRS - Centre National de la Recherche Scientifique UMR6293, INSERM - Institut National de la Santé et de la Recherche Médicale U1103, Facultés de Médecine et de Pharmacie, TSA 50400, 28 Place Henri Dunant, 63001 Clermont-Ferrand, France; IP - Institut Pascal, UCA - Université Clermont Auvergne, CNRS - Centre National de la Recherche Scientifique UMR6602, Campus Universitaire des Cézeaux, 4 avenue Blaise Pascal, TSA 60026 / CS 60026, 63178 Aubière Cedex, France; Oxford Brookes University, Department for Biological and Medical Sciences, Headington Campus, Gipsy Lane, Oxford OX3 0BP, Royaume-Uni, UK; Aarhus University, Department of Ecoscience, C.F. Møllers Allé 8, Building 1110, 8000 Aarhus C, Denmark.
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Bioimage frameworks based on artificial intelligence (AI) offer powerful tools for image segmentation, but their technical overhead often creates a gap between developers and the broader bioimaging community. Biom3d addresses this challenge by providing a modular, PyTorch-based architecture designed to enable reproducible and interoperable 2D and 3D segmentation pipelines while maintaining strict adherence to FAIR principles. The framework's engineering quality is demonstrated through low intra-module complexity, high modularity, and seamless interoperability, exemplified by the successful substitution of transformer-based MONAI models. Its architecture is organized around seven core module types, allowing fine-grained control over data handling, model configuration, optimization, and evaluation. Its default configuration, nnCore, autoconfigures optimal pipelines based on new datasets and competes with state-of-the-art 3D segmentation methods, outperforming classical tools such as NucleusJ/NODeJ and matching the robustness of nnU-Net across diverse modalities. Biom3d is accessible through multiple interfaces including a graphical user interface, a Jupyter Notebook, a command-line tool, a Docker image and as a Python library. Biom3d is compatible with OMERO (Open Microscopy Environment Remote Objects) for streamlined data management and reproducible computation, including execution on HPC servers. Collectively, these results position Biom3d as a sustainable and extensible framework for building, sharing, and reusing advanced bioimage analysis solutions.
