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Abstract #4799

Hypothalamus semi-automatic segmentation from MR images using Convolutional Neural Networks

Lívia Rodrigues1, Thiago Rezende2, Ariane Zanesco2, Ana Luiza Hernandez2, Marcondes França2, and Letícia Rittner1

1Medical Image Computing Lab, School of Electrical and Computer Engineering (FEEC), University of Campinas, Campinas, Brazil, 2Department of Neurology, School of Medical Sciences, University of Campinas, Campinas, Brazil

Hypothalamus is a small structure of the brain with important role in sleep, body temperature regulation and emotion. Some diseases as schizophrenia can be attributed to volumetric change on hypothalamus, usually measured through Magnetic Resonance Imaging (MRI). However, hypothalamic morphological landmarks are not always clear and manual segmentation can become variable, leading to inconsistent data on literature. On this project, hypothalamus was automatically segmented using convolutional neural networks (CNNs) . Three independent CNNs were trained, one for each view of volumetric MRI, obtaining final dice of 0.787 for axial view, 0.781 for sagittal and 0.747 for coronal view.

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