A dynamic Bi-CSAM-Net for brain tumor segmentation

The accurate segmentation of brain tumors from MRI scans is a critical task in medical image analysis and is essential for clinical diagnosis, treatment planning, and monitoring. Despite advancements, many existing segmentation methods focus primarily on extracting global semantic features from 3D volumetric data, often neglecting finer voxel-specific details, inter-layer connections, and subtle anatomical variations. To address these limitations, we propose a novel deep learning architecture, Bi-CSAM-Net, that integrates advanced data preprocessing techniques, attention mechanism, and the CNN model as a benchmark for segmentation tasks. The preprocessing techniques use dynamic contrast enhancement, multi-resolution data integration, and patient-specific normalization (DCA-TR) to focus on the finer voxel-specific features to enhance model performance. The attention mechanism called the BiC-AM employs a Bi-LSTM to enhance the Channel Attention Mechanism within a Convolutional Block Attention Module (CBAM) framework to capture spatial and sequential MRI data dependencies better. Our extensive evaluation of two benchmark datasets; ASNR-MICCAI-BraTs 2021 and BraTs Adult-Giloma 2023, reveals that the proposed model achieves a high segmentation Dice score for all the modalities, consistently outperforming existing methods, specifically the whole tumor (WT) and tumor core (TC) for the BraTs 2021 and 2023 datasets. The results also demonstrate significant improvements in sensitivity and specificity across various tumor sub-regions, underscoring the model’s robustness. Ablation studies further validate the contributions of our proposed modules, highlighting their effectiveness in enhancing segmentation performance. These findings underscore the potential clinical applicability of Deep Bi-CSAM-Net for accurate and reliable brain tumor segmentation. • Bi-CSAM-Net boosts spatial feature learning for accurate brain tumor boundaries. • DCA-TR preprocessing enhances contrast and reduces inter-patient intensity drift. • BiC-AM attention captures cross-channel spatial dependencies in 3-D MRI. • BraTS 2021/2023 experiments confirm superior tumor segmentation accuracy.