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Abstract #3260

A 3D Convolutional Neural Network for Hippocampal Volume Estimation

Luca Jan Schmidtke1,2,3, Ricardo Corredor-Jerez1,2,3, Jonas Richiardi1,2,3, Bènèdicte Marèchal1,2,3, Alexis Roche1,2,3,4, and Tobias Kober1,2,3

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland, 3Signal Processing Laboratory (LTS 5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4CoVii Ltd, Porto, Portugal

Accurate estimation of hippocampal volume is essential for exploiting its sensitivity to pathological changes caused by Alzheimer’s disease (AD) and other forms of dementia. We built and trained a 3D convolutional neural network for fast and accurate segmentation of the hippocampus in T1-weighted structural MR images of the brain. Compared to two software packages (MorphoBox prototype and FreeSurfer), we achieved good disease classification results based on estimated hippocampal volume in a significantly shorter amount of time.

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