Abstract #2130

Clinical Validation of the InnerEye Hippocampal Segmentation Tool

Anna Schroder¹, Hamza A. Salhab^2,3, James Moggridge^2,3, Caroline Micallef², Jiaming Wu^1,4, 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, Daniel C. Alexander¹, and Matthew Grech-Sollars^1,2

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, Machine Learning/Artificial Intelligence

Motivation: Accurate segmentation of the hippocampus provides an important biomarker in neurodegenerative diseases, e.g., Alzheimer’s disease. However, currently available tools are not robust to disease-related atrophy.

Goal(s): We aim to demonstrate the accuracy of our InnerEye hippocampal segmentation tool on clinical data.

Approach: We fine-tuned our existing model on manually segmented data and externally validated the model on a clinical dataset of patients referred to a dementia clinic. We compare our model to three commonly used segmentation tools.

Results: Our model provides significant improvements over currently available tools when tested on an external, clinical dataset.

Impact: The hippocampal segmentation model presented in this work provides significant improvements over currently available tools in an external, clinical dataset. Segmentation performance was increased, while run-times were decreased. These results support the tool as a viable alternative in clinical settings.

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