Telecentric wide-field reflected light microscopic dataset

Multi-class segmentation of unlabelled living cells in time-lapse light microscopy images is challenging due to the temporal behaviour and changes in cell life cycles and the complexity of images of this kind. The deep-learning-based methods achieved promising outcomes and remarkable success in single- and multi-class medical and microscopy image segmentation. The main objective of this study is to develop a hybrid deep-learning-based categorical segmentation and classification method for living HeLa cells in reflected light microscopy images.
A symmetric simple U-Net and three asymmetric hybrid convolution neural networks---VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net were proposed and mutually compared to find the most suitable architecture for multi-class segmentation of our datasets.  The inception module in the Inception-U-Net contained kernels with different sizes within the same layer to extract all feature descriptors. The series of residual blocks with the skip connections ..., , , # Telecentric wide-field reflected light microscopic dataset

This Telecentric bright-field_README.txt file was generated on 2023-04-20 by Ali Ghaznavi

GENERAL INFORMATION

1. Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images

2. Author Information
First-author, Corresponding author
Name: MSc. Ali Ghaznavi
Institution: Institute of Complex Systems, University of South Bohemia in České Budějovice, Zámek 136, 373 33, Nové Hrady, Czech Republic
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Co-author
Name: Dr. Renata Rychtáriková
Institution: Institute of Complex Systems, University of South Bohemia in České Budějovice, Zámek 136, 373 33, Nové Hrady, Czech Republic
Email:
Co-author
Name: Dr. Petr Císař
Institution: Institute of signal and image processing, University of South Bohemia in České Budějovice, Zámek 136, 373 33, Nové Hrady, Czech Republic
Email:

Co-author
Name: MSc. Mohammadmeh...