Abstract #0808

InnerEye as a Tool for Accurate Hippocampal Segmentation

Anna Schroder¹, James Moggridge^2,3, Jiaming Wu^1,4, Hamza A. Salhab^2,3, Sjoerd Vos⁵, Melissa Bristow⁶, Fernando Pérez-García⁶, Javier Alvarez-Valle⁶, Tarek A. Yousry^2,3, John S. Thornton^2,3, Frederik Barkhof^1,3,4,7, Matthew Grech-Sollars^1,2, and Daniel C. Alexander¹

¹Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ²Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, United Kingdom, ³Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, United Kingdom, ⁴Department of Medical Physics & Biomedical Engineering, University College London, London, United Kingdom, ⁵Centre for Microscopy, Characterisation & Analysis, University of Western Australia, Perth, Australia, ⁶Health Futures, Microsoft Research Cambridge, Cambridge, United Kingdom, ⁷Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands

Synopsis

Keywords: Segmentation, Brain, Hippocampus

Accurate hippocampal segmentation tools are critical for monitoring neurodegenerative disease progression on MRI and assessing the impact of interventional treatment. Here we present the InnerEye hippocampal segmentation model and evaluate this new model against three standard segmentation tools in an Alzheimer’s disease dataset. We found InnerEye performed best for Dice score, precision and Hausdorff distance. InnerEye performs consistently well across the different cognitive diagnoses, while performance for other methods decreased with cognitive decline.

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