Keywords: Gray Matter, Segmentation, brain
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.
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