Meyer, C., V.Hanss, E.Baudrier, B.Naegel, and P.Schultz. 2025. “DeepSCEM: A User-Friendly Solution for Deep Learning-Based Image Segmentation in Cellular Electron Microscopy.” Biology of the Cell117, no. 9: 117, e70032. https://doi.org/10.1111/boc.70032
@article{https://doi.org/10.1111/boc.70032,
author = {Meyer, Cyril and Hanss, Victor and Baudrier, Etienne and Naegel, Benoît and Schultz, Patrick},
title = {DeepSCEM: A User-Friendly Solution for Deep Learning-Based Image Segmentation in Cellular Electron Microscopy},
journal = {Biology of the Cell},
volume = {117},
number = {9},
pages = {e70032},
keywords = {cellular imaging, deep learning, electron microscopy, organelles, segmentation, software},
doi = {https://doi.org/10.1111/boc.70032},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/boc.70032},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/boc.70032},
abstract = {ABSTRACT 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.},
year = {2025}
}
Other citation format on https://onlinelibrary.wiley.com/doi/full/10.1111/boc.70032