Synthetic MRI in action: A novel framework in data augmentation strategies for robust multi-modal brain tumor segmentation.

Brain tumor diagnostics rely heavily on Magnetic Resonance Imaging (MRI) for accurate diagnosis and treatment planning due to its non-invasive nature and detailed soft tissue visualization. Integrating multiple MRI modalities enhances diagnostic precision by providing complementary perspectives on tumor characteristics and spatial relationships. However, acquiring specific modalities like T1 Contrast Enhanced (T1CE) can be challenging, as they require contrast agents and longer scan times, which can cause discomfort, particularly in vulnerable patient groups such as the elderly, pregnant women, and infants. In the medical imaging domain, researchers face significant challenges in developing robust models due to data scarcity and data sparsity. Data scarcity, arising from limited access to diverse datasets, complex annotation processes, privacy concerns, and the difficulty of acquiring certain modalities in some patient groups, impedes the development of comprehensive brain tumor segmentation models. Data sparsity, driven by the highly imbalanced distribution between tumor subregions and background levels in annotated labels, complicates accurate segmentation. The study addresses these challenges by generating synthetic T1CE scans from T1 using an image-to-image translation framework, thereby reducing the reliance on hard-to-acquire modalities. A novel patch-based data sampling approach, Adaptive Random Patch Selection (ARPS), is introduced to combat data sparsity, ensuring detailed segmentation of intricate tumor structures while maintaining context through overlapping patches and context-aware sampling strategies. The impact of these synthetic images on segmentation performance is also assessed, emphasizing their role in addressing situations where certain modalities cannot be acquired. When integrated into the nnUNet model, this approach achieves a dice similarity coefficient (DSC) of 86.47, demonstrating its efficacy in handling complex MRI scans of brain tumors. An ablation study is also conducted to assess the individual contributions of the translated images and the proposed data sampling approach. This comprehensive evaluation allows us to understand the effectiveness of ARPS and the potential synergy between multi-modal translation and brain tumor segmentation.