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Updated: Dec 17, 2025

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DDeep3M: Docker-powered deep learning for biomedical image segmentation.

Xinglong Wu1, Shangbin Chen2, Jin Huang3

  • 1School of Computer Science & Engineering, Wuhan Institute of Technology, Wuhan, Hubei, 430205, China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.

Journal of Neuroscience Methods
|June 23, 2020
PubMed
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This summary is machine-generated.

We developed DDeep3M, a Docker-based deep learning tool for biomedical image segmentation. It simplifies using deep learning models and ensures reproducible results across micro-, meso-, and macro-scale data.

Area of Science:

  • Biomedical image processing
  • Computational biology
  • Machine learning in medicine

Background:

  • Deep learning models are increasingly used in biomedical image processing.
  • Complex computational environments hinder model reproducibility and increase effort.
  • This limits accessibility for many biomedical scientists.

Purpose of the Study:

  • To present a Docker-based method for easier use and faster reproduction of deep learning models.
  • To introduce DDeep3M, a Docker-powered deep learning model for biomedical image segmentation.
  • To validate DDeep3M across different scales of biomedical data.

Main Methods:

  • Developed DDeep3M, a Docker-containerized deep learning framework.
  • Validated DDeep3M on electron microscopy data (microscale).
Keywords:
Biomedical imageDeep learningDockerSegmentation

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  • Applied DDeep3M to 3D mouse brain optical microscopy data (mesoscale) and MRI data (macroscale).

    • Main Results:

    • DDeep3M achieved high accuracy (recall/precision, Dice > 0.96) for vessel and somata segmentation in mouse brain images.
    • Demonstrated state-of-the-art performance in brain tumor segmentation on MRI data.
    • Outperformed existing models in performance and efficiency across various scales.

    Conclusions:

    • DDeep3M is a user-friendly, efficient tool for biomedical image segmentation.
    • The tool facilitates the adoption of deep learning in biomedical research.
    • DDeep3M is open-sourced with code and pretrained weights available.