Abstract #3509

Automatic segmentation of dentate nuclei for microstructure assessment: application to temporal lobe epilepsy patients

Marta Gaviraghi¹, Giovanni Savini², Gloria Castellazzi^1,3, Nicolò Rolandi⁴, Simone Sacco^5,6, Egidio D’Angelo^4,7, Fulvia Palesi⁴, Paolo Vitali², and Claudia A.M. Gandini Wheeler-Kingshott^2,4,8

¹Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy, ²Neuroradiology Unit, Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy, ³3Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, ⁴Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, ⁵UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, CA, United States, ⁶Department of Clinical Surgical Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy, ⁷Brain Connectivity Center (BCC), IRCCS Mondino Foundation, Pavia, Italy, ⁸Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy

Dentate nuclei (DN) segmentation is necessary for assessing whether DN are affected by pathologies through quantitative analysis of parameter maps, e.g. calculated from diffusion weighted imaging (DWI). This study developed a fully automated segmentation method using non-DWI (b0) images. A Convolution Neural Network was optimised on heathy subjects’ data with high spatial resolution and was used to segment the DN of Temporal Lobe Epilepsy (TLE) patients, using standard DWI. Statistical comparison of microstructural metrics from DWI analysis, as well as volumes of each DN, revealed altered and lateralised changes in TLE patients compared to healthy controls.

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