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Related Experiment Video

Updated: Jun 28, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

3D brain tumor segmentation using an improved V-Net architecture and 3D attention gate.

Sima Esmaeilzadeh Asl1, Mehdi Chehel Amirani1, Hadi Seyedarabi2

  • 1Department of Electrical Engineering, Urmia University, Urmia, Iran.

Neuroimage. Reports
|June 15, 2026
PubMed
Summary

This study presents an improved 3D V-Net for brain tumor segmentation using BraTS2021 MRI data. The enhanced model achieves high accuracy in segmenting whole tumors, tumor cores, and enhanced tumors.

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Neuroimaging

Background:

  • Accurate tumor segmentation is vital for cancer diagnosis and treatment planning.
  • Manual segmentation is time-consuming and prone to variability.
  • Automated methods offer enhanced accuracy, stability, and efficiency.

Purpose of the Study:

  • To develop and evaluate an improved 3D V-Net model for automated brain tumor segmentation.
  • To segment whole tumors, tumor cores, and enhanced tumors using multimodal MRI data.
  • To enhance the performance of the V-Net architecture for 3D medical image analysis.

Main Methods:

  • Utilized the BraTS2021 dataset for 3D brain tumor segmentation.
  • Applied N4 Bias Field Correction for MRI data preprocessing.
Keywords:
3D brain MRIAttention gateDeep learningTumor segmentationV-net

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  • Implemented an improved 3D V-Net with enhanced encoder (residual and dilated convolutions) and decoder (Attention Gate) components.
  • Inputted all four MRI modalities into the 3D improved V-Net.

    • Main Results:

    • Achieved Dice coefficients of 87% for whole tumors, 81.2% for tumor cores, and 74.43% for enhanced tumors.
    • Demonstrated the effectiveness of residual and dilated convolutions in the encoder for performance enhancement.
    • Showcased the benefit of the Attention Gate in the decoder for improved segmentation accuracy.
    • Validated the model's capability in segmenting complex 3D brain tumor structures.

    Conclusions:

    • The proposed improved 3D V-Net model effectively segments brain tumors in 3D MRI images.
    • The architectural enhancements significantly boosted the segmentation performance compared to standard V-Net.
    • This automated approach offers a promising tool for clinical applications in neuro-oncology.