Optimizing Magnetic Resonance Image Segmentation Through Scalable Deep Learning and Hierarchical Data Management

Abstract

Automated glioma segmentation in multi-modal magnetic resonance imaging (MRI) is critical in clinical neurooncology, yet it is challenged by large data volumes, tumor heterogeneity, and class imbalance. This study proposes an efficient and scalable system based on an optimized 2D U-Net architecture, integrated within a data engineering workflow utilizing HDF5. This integration enables processing large MRI datasets without loading the entire dataset into memory. The proposed method is evaluated on the public BraTS2020 benchmark using T1, T1ce, T2, and FLAIR modalities. The model achieved a Dice coefficient of 0.884, sensitivity of 0.851, specificity of 0.992, and an average Hausdorff distance of $\text{4. 2 ~ m m}$ on the test set. These results indicate segmentation accuracy consistent with expert annotations. Training was stable and converged within 15 epochs, attributed to the use of a Coefficient Dice loss function and batch normalization. Computationally, the 2D approach reduced the number of trainable parameters to approximately 7.8 million, allowing training and inference on consumer-grade GPUs with less than 8 GB of memory. The findings suggest that integrating an efficient model with optimized data management achieves a balance between segmentation accuracy and computational efficiency, making this approach suitable for resource-constrained clinical environments.