DeepSCEM: A User‐Friendly Solution for Deep Learning‐Based Image Segmentation in Cellular Electron Microscopy
Deep learning methods using convolutional neural networks are very effective for automatic image segmentation tasks with no exception for cellular electron micrographs. However, the lack of dedicated easy-to-use tools largely reduces the widespread use of these techniques. Here we present DeepSCEM, a straightforward tool for fast and efficient segmentation of cellular electron microscopy images using deep learning with a special focus on efficient and user-friendly generation and training of models for organelle segmentation.
Authors
- Victor Hanss (ORCID: https://orcid.org/0000-0001-5603-8321)
- Étienne Baudrier (ORCID: https://orcid.org/0000-0002-2038-9434)
- Cyril Meyer (ORCID: https://orcid.org/0000-0001-7262-999X)
- Benoît Naegel (ORCID: https://orcid.org/0000-0002-7695-1473)
- Patrick Schultz (ORCID: https://orcid.org/0000-0002-7310-6186)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Inserm (FR)
- Université de Haute-Alsace (FR)
- Institut de génétique et de biologie moléculaire et cellulaire (FR)
- Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (FR)
- Université de Strasbourg (FR)
Publication Details
- Journal
- Biology of the Cell
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1111/boc.70032
- Citations
- 2
- Primary Topic
- Advanced Electron Microscopy Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 5.10
Funders
- Agence Nationale de la Recherche
- Institut National de la Santé et de la Recherche Médicale
- Université de Strasbourg
- Ligue Contre le Cancer
- Centre National de la Recherche Scientifique
- French Infrastructure for Integrated Structural Biology