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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