Keywords: Segmentation, Machine Learning/Artificial Intelligence, Choroid Plexus
The Choroid Plexus (ChP) is a brain vascular tissue involved in regulatory processes. ChP Volume (ChPV) modifications are related to neurodegenerative disorders, consequently, it was suggested the use of ChPV as biomarker. This work proposes a method for the automatic segmentation of ChP based on Deep-Learning Neural-Networks (DNNs) hyperparameters optimization. Ninety-Six hyperparameters and architectures combinations were trained on T1-w MRI in MONAI, first selection was made on bias and variance and best DNNs were ensembled by major voting. Ensemble model outperforms single DNNs and freely available software (FreeSurfer, Gaussian Mixture Model), highlighting the ensembles DNNs exploitability to automatically estimate ChPV.
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