CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy
Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inher...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2025-06-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/99848 |
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| author | Cyril Achard Timokleia Kousi Markus Frey Maxime Vidal Yves Paychere Colin Hofmann Asim Iqbal Sebastien B Hausmann Stéphane Pagès Mackenzie Weygandt Mathis |
| author_facet | Cyril Achard Timokleia Kousi Markus Frey Maxime Vidal Yves Paychere Colin Hofmann Asim Iqbal Sebastien B Hausmann Stéphane Pagès Mackenzie Weygandt Mathis |
| author_sort | Cyril Achard |
| collection | DOAJ |
| description | Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inherent complexity of generating 3D ground truth data and quantifying nuclei in 3D volumes. We developed a Python package called CellSeg3D that provides access to these models in Jupyter Notebooks and in a napari GUI plugin. Recognizing the scarcity of high-quality 3D ground truth data, we created a fully human-annotated mesoSPIM dataset to advance evaluation and benchmarking in the field. To assess model performance, we benchmarked our approach across four diverse datasets: the newly developed mesoSPIM dataset, a 3D platynereis-ISH-Nuclei confocal dataset, a separate 3D Platynereis-Nuclei light-sheet dataset, and a challenging and densely packed Mouse-Skull-Nuclei confocal dataset. We demonstrate that our self-supervised model, WNet3D – trained without any ground truth labels – achieves performance on par with state-of-the-art supervised methods, paving the way for broader applications in label-scarce biological contexts. |
| format | Article |
| id | doaj-art-ec9093310ef741babed0a29b14768417 |
| institution | DOAJ |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-ec9093310ef741babed0a29b147684172025-08-20T03:23:18ZengeLife Sciences Publications LtdeLife2050-084X2025-06-011310.7554/eLife.99848CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopyCyril Achard0https://orcid.org/0009-0003-6992-6928Timokleia Kousi1Markus Frey2https://orcid.org/0000-0003-0291-3391Maxime Vidal3Yves Paychere4Colin Hofmann5Asim Iqbal6https://orcid.org/0000-0003-2174-4554Sebastien B Hausmann7Stéphane Pagès8https://orcid.org/0000-0003-0618-777XMackenzie Weygandt Mathis9https://orcid.org/0000-0001-7368-4456Brain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandWyss Center for Bio and Neuroengineering, Geneva, SwitzerlandBrain Mind Institute and Neuro X, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, SwitzerlandUnderstanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inherent complexity of generating 3D ground truth data and quantifying nuclei in 3D volumes. We developed a Python package called CellSeg3D that provides access to these models in Jupyter Notebooks and in a napari GUI plugin. Recognizing the scarcity of high-quality 3D ground truth data, we created a fully human-annotated mesoSPIM dataset to advance evaluation and benchmarking in the field. To assess model performance, we benchmarked our approach across four diverse datasets: the newly developed mesoSPIM dataset, a 3D platynereis-ISH-Nuclei confocal dataset, a separate 3D Platynereis-Nuclei light-sheet dataset, and a challenging and densely packed Mouse-Skull-Nuclei confocal dataset. We demonstrate that our self-supervised model, WNet3D – trained without any ground truth labels – achieves performance on par with state-of-the-art supervised methods, paving the way for broader applications in label-scarce biological contexts.https://elifesciences.org/articles/99848self-supervised learningartificial intelligenceneurosciencemesoSPIMconfocal microscopyplatynereis |
| spellingShingle | Cyril Achard Timokleia Kousi Markus Frey Maxime Vidal Yves Paychere Colin Hofmann Asim Iqbal Sebastien B Hausmann Stéphane Pagès Mackenzie Weygandt Mathis CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy eLife self-supervised learning artificial intelligence neuroscience mesoSPIM confocal microscopy platynereis |
| title | CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy |
| title_full | CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy |
| title_fullStr | CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy |
| title_full_unstemmed | CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy |
| title_short | CellSeg3D, Self-supervised 3D cell segmentation for fluorescence microscopy |
| title_sort | cellseg3d self supervised 3d cell segmentation for fluorescence microscopy |
| topic | self-supervised learning artificial intelligence neuroscience mesoSPIM confocal microscopy platynereis |
| url | https://elifesciences.org/articles/99848 |
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