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Updated: Feb 20, 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

3.6K

Brain tumor segmentation using cascaded deep convolutional neural network.

Saddam Hussain, Syed Muhammad Anwar, Muhammad Majid

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
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    This study introduces an automated brain tumor segmentation algorithm using deep convolutional neural networks (DCNNs). The method enhances detection accuracy for gliomas, improving patient survival rates.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Neuro-oncology

    Background:

    • Gliomas are aggressive brain tumors with poor prognoses.
    • Accurate segmentation is vital for effective clinical management.
    • Tumor irregularity and location pose segmentation challenges.

    Purpose of the Study:

    • To develop an automated brain tumor segmentation algorithm.
    • To improve the accuracy of glioma detection and segmentation.
    • To address overfitting issues in deep learning models with limited data.

    Main Methods:

    • Utilized deep convolutional neural networks (DCNNs) for automated segmentation.
    • Implemented max-out and drop-out layers to mitigate overfitting.
    • Employed a patch-based training strategy with 37x37 and 19x19 patches.

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    Last Updated: Feb 20, 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

    3.6K
  • Incorporated image normalization, bias field correction, and morphological operators for preprocessing and postprocessing.

    • Main Results:

    • The proposed DCNN algorithm demonstrated superior performance on the BRATS 2013 dataset.
    • Achieved state-of-the-art results compared to similar methods.
    • Effectively handled challenges of irregular tumor shapes and varying locations.

    Conclusions:

    • The developed automated segmentation algorithm shows significant promise for clinical application.
    • Deep learning models, with appropriate regularization, can effectively segment brain tumors.
    • This approach offers a more accurate and efficient method for glioma detection.