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Dilated Convolutional V-Net with Transformer Integration for Brain Tumor Segmentation.

Dolly Uppal¹, Surya Prakash¹

¹Discipline of Computer Science & Engineering, Indian Institute of Technology Indore, Indore 453552, India.

Computers in Biology and Medicine

|October 19, 2025

Summary

This study introduces DCTransVNet, a deep learning model for segmenting brain tumors in MRI scans. It enhances feature representation for more accurate tumor detection and treatment planning.

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Area of Science:

Medical Imaging
Artificial Intelligence
Neuro-oncology

Background:

Brain tumors necessitate early detection for effective treatment, but their complex nature complicates segmentation in MRI scans.
Existing deep learning models struggle to capture essential channel-wise feature interdependence for accurate spatial and channel feature representation.
Automated segmentation is vital for timely diagnosis and treatment planning in neuro-oncology.

Purpose of the Study:

To propose Dilated Convolutional Transformer V-Net (DCTransVNet) for improved volumetric brain tumor segmentation in multimodal MRI scans.
To enhance feature learning by effectively capturing spatial and channel-wise interdependencies.
To improve segmentation performance using a novel Augmented Brain Tumor Segmentation Generative Adversarial Network (AuBTS-GAN).

Keywords:

Brain tumor segmentation Deep learning Generative adversarial network Multimodal MRI Residual block Transformer

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Main Methods:

Developed DCTransVNet, an encoder-decoder network integrating 3D dilated residual blocks and Efficient Paired-Attention blocks.
Incorporated a Global Context module to capture long-range dependencies for comprehensive feature representation.
Utilized AuBTS-GAN to generate synthetic MRI data for augmenting training datasets.

Main Results:

DCTransVNet demonstrated superior performance in volumetric brain tumor segmentation compared to state-of-the-art methods.
The model effectively captured multi-scale contextual features and channel-wise feature interdependence.
Experiments on benchmark datasets (BraTS, MSD) validated the proposed method's efficacy.

Conclusions:

DCTransVNet offers a significant advancement in automated brain tumor segmentation from multimodal MRI.
The proposed attention mechanisms and generative adversarial network contribute to enhanced segmentation accuracy.
This work provides a robust tool for clinical diagnosis and treatment planning in neuro-oncology.