Cargando…
Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes
Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting superior performance, or even human-level accuracy...
Autores principales: | Franco-Barranco, Daniel, Muñoz-Barrutia, Arrate, Arganda-Carreras, Ignacio |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9546980/ https://www.ncbi.nlm.nih.gov/pubmed/34855126 http://dx.doi.org/10.1007/s12021-021-09556-1 |
Ejemplares similares
-
Deep-Learning-Based Segmentation of Small Extracellular Vesicles in Transmission Electron Microscopy Images
por: Gómez-de-Mariscal, Estibaliz, et al.
Publicado: (2019) -
Deep neural network automated segmentation of cellular structures in volume electron microscopy
por: Gallusser, Benjamin, et al.
Publicado: (2022) -
Applications of Light-Sheet Microscopy in Microdevices
por: Albert-Smet, Ignacio, et al.
Publicado: (2019) -
Automatic Reconstruction of Mitochondria and Endoplasmic Reticulum in Electron Microscopy Volumes by Deep Learning
por: Liu, Jing, et al.
Publicado: (2020) -
Single Plane Illumination Microscopy for Microfluidic Device Imaging
por: Gomez-Cruz, Clara, et al.
Publicado: (2022)