Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex
Zeng, X.; Puonti, O.; Sayeed, A.; Herisse, R.; Mora, J.; Evancic, K.; Varadarajan, D.; Balbastre, Y.; Costantini, I.; Scardigli, M.; Ramazzotti, J.; DiMeo, D.; Mazzamuto, G.; Pesce, L.; Brady, N.; Cheli, F.; Pavone, F. S.; Hof, P. R.; Frost, R.; Augustinack, J.; van der Kouwe, A.; Iglesias, J. E.; Fischl, B.
Show abstract
Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Lever-aging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 {micro}m, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.
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