Abstract #2477

Study of the preprocessing impact on the Deep Learning automatic segmentation of Choroid Plexus in Multiple Sclerosis

Valentina Visani¹, Francesca Benedetta Pizzini², Annalisa Colombi³, Valerio Natale², Agnese Tamanti³, Alessandra Bertoldo⁴, Corina Marjin³, Giuseppe Kenneth Ricciardi⁵, Massimiliano Calabrese³, and Marco Castellaro¹

¹Department of Information Engineering, University of Padova, Padova, Italy, ²Department of Diagnostic and Public Health, University of Verona, Verona, Italy, ³Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy, ⁴Department of Information Engineering, Padova Neuroscience Center, University of Padova, Padova, Italy, ⁵Neuroradiology Section, Diagnostic Pathology Department, University of Verona, Verona, Italy

Synopsis

Keywords: Data Processing, Machine Learning/Artificial Intelligence, Multiple Sclerosis

Motivation: The Choroid Plexus (ChP) is a vascular structure involved in brain regulatory functions. The relation between ChP Volume and brain disorders raises the interest on this structure and the need for an accurate segmentation, questioning whether to introduce a preprocessing step.

Goal(s): This work studies the preprocessing impact on the ChP segmentation with Deep Neural Networks (DNN) ensemble.

Approach: Three different preprocessing steps (brain extraction, N4 intensity correction, combination of both) were applied to 128 T1-w MRI images before DNN training. These approaches performances were compared to that without preprocessing.

Results: The preprocessing step does not improve DNN performance for the ChP segmentation.

Impact: The preprocessing steps of brain extraction and N4 intensity normalization correction on T1-w MRI images do not have an impact on Deep Neural Networks performance during the automatic segmentation of Choroid Plexus on Multiple Sclerosis patients.

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