Abstract #3260

A 3D Convolutional Neural Network for Hippocampal Volume Estimation

Luca Jan Schmidtke^1,2,3, Ricardo Corredor-Jerez^1,2,3, Jonas Richiardi^1,2,3, Bènèdicte Marèchal^1,2,3, Alexis Roche^1,2,3,4, and Tobias Kober^1,2,3

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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