Multi-Task Learning with Hierarchical Label Consolidation for Efficient and Accurate Brain MRI Segmentation of 169 Regions

Motivation: Efficient brain MRI segmentation is crucial, but current models with many labels require high computational resources, limiting their practicality in resource-constrained settings. Goal(s): To reduce computational demands without compromising accuracy by using label consolidation and multi-task learning in brain MRI segmentation models. Approach: We consolidated 169 labels into 65 using hierarchical label consolidation and employed multi-task learning within a 3D U-Net framework, significantly reducing memory usage and processing time. Results: Our model outperforms traditional models in accuracy, uses less GPU and CPU memory, and cuts CPU processing time by 87%, enabling faster processing on limited-resource systems. Impact: The method enhances efficiency and accuracy in brain MRI segmentation, allowing integration of additional tasks like cortical thickness estimation, leading to a multifunctional open-source brain MRI processing toolbox.