Partial Information Decomposition as a Multi-Contrast 3D MRI Selection Strategy for Resource-Constrained Deep Neural Network Training in Brain Tumor Segmentation
Quick Answer
This paper shows that A Partial Information Decomposition framework was utilized to select T1c+T2-FLAIR MRI inputs for training lightweight 3D U-Nets, achieving a mean Dice score of 0.676, only slightly lower than the full input configuration's 0.687.
Quick Take
This approach demonstrates an effective strategy for optimizing MRI input selection in resource-constrained environments, enhancing brain tumor segmentation performance.
Key Points
- Partial Information Decomposition ranks MRI input pairs by their information contributions.
- T1c+T2-FLAIR was the top-performing input pair for brain tumor segmentation.
- Eleven lightweight 3D U-Nets were trained with varying input configurations.
- The selected input pair achieved a mean Dice score of 0.676.
- Shapley analysis confirmed T2-FLAIR and T1c as the most influential inputs.
Paper Resources
Source Excerpt
Multi-contrast 3D MRI segmentation can be computationally demanding when all available sequences are used. We evaluate a pre-training Partial Information Decomposition framework that ranks input pairs according to their redundant, unique, and synergistic information about regional tumor burden and selects the highest-ranked pair for downstream training. Applied to T1n, T1c, T2w, and T2-FLAIR MRI, the framework selected T1c+T2-FLAIR. We then trained eleven architecturally identical lightweight 3D
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