Realistic-Shape Bacterial Biofilm Simulator for Deep Learning-Based 3D Single-Cell Segmentation
Author:
Affiliation:
1. University of Virginia,Electrical and Computer Engineering,Charlottesville,USA
2. Florida State University,Department of Statistics,Tallahassee,USA
3. University of Virginia,Department of Chemistry,Charlottesville,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9761376/9761399/09761655.pdf?arnumber=9761655
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1. Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
2. NuSeT: A deep learning tool for reliably separating and analyzing crowded cells
3. Cellpose: a generalist algorithm for cellular segmentation
4. DeepSynth: Three-dimensional nuclear segmentation of biological images using neural networks trained with synthetic data
5. Computational Modeling of Synthetic Microbial Biofilms
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1. Synthesizing Unpaired Images of Bacterial Biofilms with Auxiliary Diffusion Models;2024 IEEE International Symposium on Biomedical Imaging (ISBI);2024-05-27
2. DeepSeeded: Volumetric segmentation of dense cell populations with a cascade of deep neural networks in bacterial biofilm applications;Expert Systems with Applications;2024-03
3. Single‐cell segmentation in bacterial biofilms with an optimized deep learning method enables tracking of cell lineages and measurements of growth rates;Molecular Microbiology;2023-04-17
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