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Related Experiment Video

Updated: Dec 2, 2025

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CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image.

Pengyi Zhang1,2, Yunxin Zhong1,2, Yulin Deng1,2

  • 1School of Life Science, Beijing Institute of Technology, Haidian District, Beijing 100081, China.

Diagnostics (Basel, Switzerland)
|November 6, 2020
PubMed
Summary

Synthesizing diverse radiological images from a single source using CoSinGAN enables accurate COVID-19 infection segmentation. This deep learning approach significantly improves detection from limited computed tomography (CT) data, crucial for early pandemic stages.

Keywords:
conditional distributioncovid19 infection segmentationgenerative modelsingle radiological image

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Computed tomography (CT) is vital for COVID-19 diagnosis.
  • Automated COVID-19 detection from CT scans using deep learning accelerates examination.
  • Acquiring extensive training data for deep models is challenging during early pandemic phases.

Purpose of the Study:

  • To explore the feasibility of learning deep models for lung and COVID-19 infection segmentation from limited radiological images.
  • To address the challenge of data scarcity by synthesizing diverse radiological images.
  • To propose and evaluate a novel conditional generative model, CoSinGAN, for this purpose.

Main Methods:

  • Developed CoSinGAN, a conditional generative model capable of learning from a single radiological image and its corresponding annotation mask.
  • CoSinGAN synthesizes high-resolution (512 × 512) diverse radiological images matching input conditions.
  • Evaluated CoSinGAN's efficacy using 5-fold cross-validation on the COVID-19-CT-Seg dataset and independent testing on the MosMed dataset.

Main Results:

  • Deep learning models (2D and 3D U-Net) trained on CoSinGAN-generated data achieved notable infection segmentation performance.
  • The method significantly surpassed the COVID-19-CT-Seg-Benchmark, which was trained on a much larger dataset (average 704 slices vs. 4 synthesized slices).
  • CoSinGAN effectively learned segmentation from very few radiological images.

Conclusions:

  • CoSinGAN demonstrates strong potential for learning COVID-19 infection segmentation from limited radiological images.
  • This approach is particularly valuable for the early stages of a pandemic when data collection is difficult.
  • The synthesized data enables robust deep learning model training, improving diagnostic efficiency.