GIF_boost : A Generalisable Hybrid Brain Tissue Segmentation with DeepLearning
Jiaming Wu, Giuseppe Pontillo, Zoe Mendelsohn, Yipeng Hu, Frederik Barkhof, Ferrán Prados · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
Image segmentation and parcellation can provide quantitative assessment of the brain and can guide diagnosis and treatment decision-making. Geodesic Information Flow (GIF) is a freely available brain tissue segmentation and parcellation MRI-based tool using a classical label fusion approach. In this work, we introduce GIF_boost, a hybrid solution that takes advantages of deep learning to accelerate the bottleneck step of the template library registration. We compared GIF_boost with the original version of GIF and FreeSurfer (a state-of-the-art method). GIF_boost performed parcellation minimum 16 times faster. Parcellations had a similar Dice coefficient and Hausdorff distance and an improved volumetric quantification.