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

Abstract

Brain tumors are caused by rapid and uncontrolled cell growth that may pose a potential threat to life if not treated at an early stage. The complex structure of the brain poses a significant challenge in accurately delineating both tumor and non-tumor regions. Detecting brain tumors is complicated by their irregular shapes and varied locations within the brain. Automated brain tumor segmentation from Magnetic Resonance Imaging (MRI) scans is crucial for timely clinical diagnosis and treatment planning. Recent advancements in deep learning methods have shown remarkable success in automated brain tumor segmentation. However, these methods are unable to effectively capture channel-wise feature interdependence, which is required for efficiently learning spatial and channel feature representations. Thus, we propose Dilated Convolutional Transformer V-Net, named DCTransVNet, for volumetric brain tumor segmentation in Multimodal MRI scans. DCTransVNet is structured as an encoder–decoder network, where the encoder integrates four 3D dilated residual blocks to effectively extract multi-scale contextual feature representation from MRI scans. Following each 3D dilated residual block, an Efficient Paired-Attention block is employed to emphasize spatial and channel-wise information, contributing to more discriminative feature representation. Further, the Global Context module captures long-range dependencies and achieves a comprehensive global feature representation. Additionally, we propose a novel Augmented Brain Tumor Segmentation Generative Adversarial Network (AuBTS-GAN) to generate realistic synthetic images for enhanced segmentation performance. We perform extensive experiments and ablation studies on benchmark datasets, including BraTS-2019, BraTS-2020, BraTS-2021 from the Multimodal Brain Tumor Segmentation Challenge (BraTS) and the Medical Segmentation Decathlon (MSD) BraTS dataset. The results show the efficacy of the proposed DCTransVNet compared to existing state-of-the-art approaches. © 2025 Elsevier B.V., All rights reserved.