Can we distinguish intra- and inter-variability with log Jacobian maps derived from brain morphological deformations using pediatric MRI scans?

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Dimitrijevic, Andjela | Dégeilh, Fanny | de Leener, Benjamin

Edité par CCSD -

International audience. Motivation: Importance of analyzing deformation fields derived from both intra- and inter-individual pairs of T1-weighted images which could offer insights into typical and atypical neurodevelopment.Goal(s): We aimed to fine-tune a 3D CNN to classify intra and inter-individual variability based on log Jacobian maps from deformation fields of pediatric longitudinal MRI.Approach: 279 log Jacobian maps of both intra- and inter-individual pairs are extracted using ANTs. A 3D CNN is trained in two ways (overlap and no overlap) for binary classification using 10-fold cross-validation.Results: As expected, the overlap scenario had higher accuracy and F1 score compared to no-overlap, nonetheless both achieving good results.Impact: This project's focus on pediatric MRI scans aims to understand deformations in medical imaging, advancing diagnostic tools. By distinguishing intra and inter-individual variability using log Jacobian-derived deformation patterns, it subsequently aims to model typical neurodevelopment through trajectories for deviation prediction.

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