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Proceedings Paper

Hypothalamus fully automatic segmentation from MR images using a U-Net based architecture
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Paper Abstract

Hypothalamus is a small structure of the brain with an important role in sleep, appetite, body temperature regulation and emotion. Some neurological diseases, such as Schizophrenia, Alzheimer and Amyotrophic Lateral Sclerosis (ALS) may be related to hypothalamic volume variation. However, hypothalamic morphological landmarks are not always clear on magnetic resonance (MR) images and manual segmentation can become variable, leading to inconsistent findings in the literature. In this work, we propose a fully automatic segmentation method, with no human interaction, to segment hypothalamus in MR images using convolutional neural networks (CNNs). The best performance was obtained by a consensus model using the majority voting from three 2D-CNNs trained on axial, coronal and sagittal MRI slices, achieving a DICE coefficient of 0.77. To the best of our knowledge, this is the first work to fully automatically segment the hypothalamus.

Paper Details

Date Published: 3 January 2020
PDF: 7 pages
Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300J (3 January 2020); doi: 10.1117/12.2542585
Show Author Affiliations
Livia Rodrigues, Univ. Estadual de Campinas (Brazil)
Thiago Rezende, Univ. Estadual de Campinas (Brazil)
Ariane Zanesco, Univ. Estadual de Campinas (Brazil)
Ana Luisa Hernandez, Univ. Estadual de Campinas (Brazil)
Marcondes Franca, Univ. Estadual de Campinas (Brazil)
Leticia Rittner, Univ. Estadual de Campinas (Brazil)


Published in SPIE Proceedings Vol. 11330:
15th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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