Fast Brain Tumor Segmentation with Model Optimization

Abstract

Segmenting brain tumors is important for effective diagnosis and treatment planning. Conventional 3D segmentation models achieve high accuracy but are computationally intensive, often limiting real-time applicability. In this study, pseudo-3D convolutions, which consist of spatial and depthwise convolutions, are used in place of traditional 3D convolutions. Adaptive Dilated Multi-Fiber (DMF) units dynamically extract multi-scale features and parallel Multi-Fiber (MF) units combine them with weighted sum. Efficient Channel Attention (ECA) and Cross Attention improve feature selection and fusion in decoder and encoder. Structured pruning reduces superfluous parameters and Quantization Aware Training (QAT) increases the speed of inference with the model converted to INT8 precision. Combination of Dice Loss and Boundary Loss enhances the precision of tumor boundaries. The framework has been evaluated on the BraTS 2021 data validation set and achieved high Dice scores of Whole Tumor 91.85%, Tumor Core 88.52%, and Enhancing Tumor 85.55%, with Hausdorff95 values of 2.58 mm, 3.53 mm, and 3.65 mm. Our proposed model requires only 3.57M parameters and 21.26 GFLOPs, achieving an inference time of 0.016 seconds per 3D volume while maintaining precision alongside efficiency to clinical application.