Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

Impact of Training Data, Ground Truth and Shape Variability in the Deep Learning-Based Semantic Segmentation of HeLa Cells Observed with Electron Microscopy

This paper investigates the impact of the amount of training data and the shape variability on the segmentation provided by the deep learning architecture U-Net. Further, the correctness of ground truth (GT) was also evaluated. The input data consisted of a three-dimensional set of images of HeLa ce...

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Bibliographic Details
Main Authors: Cefa Karabağ, Mauricio Alberto Ortega-Ruíz, Constantino Carlos Reyes-Aldasoro
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Journal of Imaging
Subjects:
U-Net
deep learning
training
cell segmentation
HeLa cells
Online Access:https://www.mdpi.com/2313-433X/9/3/59

Internet

https://www.mdpi.com/2313-433X/9/3/59

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