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Updated: Jul 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Enhanced cell segmentation with limited annotated data using generative adversarial networks.

Abolfazl Zargari^1,2, Najmeh Mashhadi³, S Ali Shariati^4,5,6,2

¹Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, USA.

Biorxiv : the Preprint Server for Biology

|August 7, 2023

Summary

This summary is machine-generated.

Generative Adversarial Networks (GANs) create realistic synthetic cell images to train deep learning models, overcoming limited annotated data challenges in bioimage analysis. This approach enhances cell segmentation accuracy and robustness for microscopy imaging.

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Last Updated: Jul 19, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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Published on: July 5, 2024

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Bioimage analysis
Deep learning
Computational biology

Background:

Deep learning significantly advances bioimage analysis, particularly in microscopy tasks like cell segmentation.
Developing generalizable deep learning models is hindered by the lack of large, diverse annotated cell image datasets.
Generative Adversarial Networks (GANs) offer a potential solution by generating realistic synthetic images for training.

Conclusions:

The proposed customized CycleGAN method effectively addresses the paucity of annotated data in microscopy imaging.
This approach enhances the variability and authenticity of training samples, leading to improved segmentation model performance.
The method accelerates the development of robust foundation models for cell segmentation in microscopy images.