VGG-UNet/VGG-SegNet Supported Automatic Segmentation of Endoplasmic Reticulum Network in Fluorescence Microscopy Images

This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a c...

Descripción completa

Detalles Bibliográficos
Autores principales: Daniel, Jesline, Rose, J. T. Anita, Vinnarasi, F. Sangeetha Francelin, Rajinikanth, Venkatesan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200602/
https://www.ncbi.nlm.nih.gov/pubmed/35800206
http://dx.doi.org/10.1155/2022/7733860

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200602/
https://www.ncbi.nlm.nih.gov/pubmed/35800206
http://dx.doi.org/10.1155/2022/7733860

Ejemplares similares