Abstract #2658

A 3D Convolutional Deep Neural Network for lumbar plexus segmentation

Kevin Bronik¹, Marios Yiannakas², Claudia A. M. Gandini Wheeler-Kingshott³, Daniel Alexander¹, and Ferran Prados Carrasco⁴

¹Center for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, UCL, London, United Kingdom, ²Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, UCL, London, United Kingdom, ³Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy, Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy, NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, United Kingdom, ⁴Universitat Oberta de Catalunya, Barcelona, Spain, Center for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, UCL, London, United Kingdom

A fully automated approach for lumbar plexus segmentation that could facilitate quantitative MRI assessments is presented. The approach is based on a 3D cascaded Convolutional Deep Neural Network (CNN) with concatenated loss function and optimized data augmentation policy. The method offers single modality segmentation and uses as input a commonly used 3D acquisition for peripheral nerve imaging. The performance analysis of the predicted segmentation results in comparison to manually segmented masks revealed 68% agreement. Future improvements in the predictive performance of the proposed method are anticipated by involving much larger datasets to reduce overfitting and improve CNN generalization ability.

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