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

Livia Rodrigues; Thiago Rezende; Ariane Zanesco; Ana Luisa Hernandez; Marcondes Franca; Leticia Rittner

doi:10.1117/12.2542585

3 January 2020 Hypothalamus fully automatic segmentation from MR images using a U-Net based architecture

Livia Rodrigues, Thiago Rezende, Ariane Zanesco, Ana Luisa Hernandez, Marcondes Franca, Leticia Rittner

Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 113300J (2020) https://doi.org/10.1117/12.2542585
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia

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.

Citation Download Citation

Livia Rodrigues, Thiago Rezende, Ariane Zanesco, Ana Luisa Hernandez, Marcondes Franca, and Leticia Rittner "Hypothalamus fully automatic segmentation from MR images using a U-Net based architecture", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300J (3 January 2020); https://doi.org/10.1117/12.2542585

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