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Updated: Mar 19, 2026

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

Published on: November 30, 2022

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Brain tumor segmentation with Deep Neural Networks.

Mohammad Havaei1, Axel Davy2, David Warde-Farley3

  • 1Université de Sherbrooke, Sherbrooke, Qc, Canada.

Medical Image Analysis
|June 17, 2016
PubMed
Summary
This summary is machine-generated.

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This study introduces a fast, automatic brain tumor segmentation method using novel Deep Neural Networks (DNNs), specifically Convolutional Neural Networks (CNNs), for glioblastoma detection in MR images.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuro-oncology

Background:

  • Accurate brain tumor segmentation is crucial for diagnosis and treatment planning.
  • Glioblastomas present challenges due to their variable appearance and location in MR images.
  • Existing segmentation methods often lack efficiency and flexibility.

Purpose of the Study:

  • To develop a fully automatic and efficient brain tumor segmentation method for glioblastomas.
  • To leverage Deep Neural Networks (DNNs), particularly Convolutional Neural Networks (CNNs), for improved segmentation performance.
  • To address the challenges of tumor variability and data imbalance in medical image analysis.

Main Methods:

  • Proposed a novel CNN architecture integrating local and global contextual features.
Keywords:
Brain tumor segmentationCascaded convolutional neural networksConvolutional neural networksDeep neural networks

Related Experiment Videos

Last Updated: Mar 19, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

3.7K
  • Implemented a convolutional fully connected layer for a 40x speed-up.
  • Utilized a 2-phase training procedure to handle label imbalance.
  • Explored a cascade architecture using sequential CNNs.

    • Main Results:

    • Achieved state-of-the-art performance on the 2013 BRATS dataset.
    • The novel CNN architecture demonstrated superior accuracy in brain tumor segmentation.
    • The method was over 30 times faster than existing approaches.

    Conclusions:

    • The developed DNN-based method offers a highly efficient and accurate solution for brain tumor segmentation.
    • The novel CNN architecture and training strategy effectively address segmentation challenges.
    • This approach has significant potential for clinical applications in neuro-oncology.