WikiSysop: Created page with "== Citation == Morales-Martı́nez, A., Garduño, E., Carazo, J.M., Sorzano, C.O.S. and Vilas, J.L. 2025. Membrane and vesicle structure detection in cryo-electron tomography based on deep learning. J. Structural Biology. (2025), 108258. == Abstract == Cryo-electron tomography (cryo-ET) is a microscopy technique that enables the acquisition of 3D images of biological samples. Research in cell biology has shown that cellular processes are carried out by groups of macro..."

2025-11-12T16:33:41Z

Created page with "== Citation == Morales-Martı́nez, A., Garduño, E., Carazo, J.M., Sorzano, C.O.S. and Vilas, J.L. 2025. Membrane and vesicle structure detection in cryo-electron tomography based on deep learning. J. Structural Biology. (2025), 108258. == Abstract == Cryo-electron tomography (cryo-ET) is a microscopy technique that enables the acquisition of 3D images of biological samples. Research in cell biology has shown that cellular processes are carried out by groups of macro..."

New page

== Citation ==

Morales-Martı́nez, A., Garduño, E., Carazo, J.M., Sorzano, C.O.S. and Vilas, J.L. 2025. Membrane and vesicle structure detection in cryo-electron tomography based on deep learning. J. Structural Biology. (2025), 108258.

== Abstract ==

Cryo-electron tomography (cryo-ET) is a microscopy technique that enables the acquisition of 3D images of
biological samples. Research in cell biology has shown that cellular processes are carried out by groups of
macromolecules that interact in a crowded environment. In such an environment, where multiple biological
macromolecules coexist and intertwine, semantic segmentation becomes even more challenging but crucial
to understanding the structure and function of macromolecular complexes. However, manual semantic
segmentation can be time-consuming, highly subjective, and prone to variability, which poses significant
obstacles in studies dealing with large volumes of data. In contrast, automated algorithms such as Convolutional
Neural Networks (CNNs) can process large-scale datasets with minimal human resources, thereby reducing the
subjectivity associated with manual segmentation. In this work, we propose a convolutional neural network
architecture that combines the features of U-Net, DeepLab, SegNet, Gated-SCNN, LSTM (Long Short-Term
Memory), RNN (Recurrent Neural Network), and GAN (Generative Adversarial Network) architectures. This
hybrid architecture effectively learns to identify different types of membranes and can replicate the behavior
of a skilled human annotator. This system demonstrates a strong ability to segment various cellular membranes
and vesicle structures.

== Keywords ==

== Links ==

https://www.sciencedirect.com/science/article/pii/S1047847725000930

== Related software ==

== Related methods ==

== Comments ==

2025Morales Membranes - Revision history