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Publication Detail
A 3D Convolutional Deep Neural Network for lumbar plexus segmentation
  • Publication Type:
    Conference presentation
  • Publication Sub Type:
    Presentation
  • Authors:
    Bronik K, Yiannakas M, Gandini Wheeler-Kingshott CAM, Alexander D, Prados Carrasco F
  • Date:
    08/08/2020
  • Status:
    Published
  • Name of Conference:
    ISMRM & SMRT Virtual Conference & Exhibition 2020
  • Conference place:
    Paris, ISMRM/SMRT Virtual
  • Conference start date:
    08/08/2020
  • Conference finish date:
    14/08/2020
  • Language:
    English
  • Keywords:
    3D Convolutional Deep Neural, concatenated loss function, peripheral nerve imagin, cascaded CNN
  • Conference URL:
  • Addresses:
    Kevin Bronik
    14 Perth road
    London
    London
    N22 5RB
    United Kingdom
Abstract
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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Dept of Computer Science
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Dept of Computer Science
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Dept of Med Phys & Biomedical Eng
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Neuroinflammation
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