Meyer Cyril, Hanss Victor, Baudrier Etienne, Naegel Benoît, Schultz Patrick
IRIMAS, Université de Haute-Alsace, UR 7499, Mulhouse, France.
IGBMC, Université de Strasbourg, CNRS UMR 7104, Inserm UMR-S 1258, Illkirch, France.
Biol Cell. 2025 Sep;117(9):e70032. doi: 10.1111/boc.70032.
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.
使用卷积神经网络的深度学习方法对于自动图像分割任务非常有效，细胞电子显微镜图像也不例外。然而，缺乏专用的易于使用的工具在很大程度上减少了这些技术的广泛应用。在这里，我们展示了DeepSCEM，这是一种使用深度学习对细胞电子显微镜图像进行快速有效分割的直接工具，特别关注用于细胞器分割的模型的高效且用户友好的生成和训练。
