Automated Grading of Lumbar Disc Degeneration Using a Push-Pull Regularization Network Based on MRI

Lower back pain is one of the most widely experienced health problems and is strongly associated with intervertebral disc (IVD) degeneration. The quantification of the degeneration is valuable for estimation of the material properties of the IVDs and then to perform biomechanical simulation of the spinal conditions and treatments. The MR image characteristics of relatively high intraclass variability and small interclass differences pose challenges for the classification algorithm to perform automatic grading of degenerated IVD.

To assess the feasibility and improvement of a computer-assisted IVD degeneration grading method based on proposed push-pull regularization (PPR) strategy.

Retrospective.

In total, 500 subjects (350 for training, 70 for validation, and 80 for test in a 10-time 10-fold cross validation setting) with varied lumbar disorders were included.

3.0T; T₂ -weighted spin echo sequence.

IVD degeneration grading was taken as a classification task of five classes according to the Pfirrmann grading system in this study. The classification results of deep-learning models with and without PPR were compared with the classifications made by three experienced spinal radiologists.

Paired t-tests.

The classification results show that in four classical CNN models of VGG-M, VGG-16, GoogleNet, and ResNet-34, by embedding a PPR strategy, the accuracies of grade II and III IVD classification were improved by more than 10% (P < 0.05), and that the overall accuracy (grades I to V) was improved by over 8% (P < 0.05).

The embedded PPR significantly improved the classification performance, which enhanced CNN representation capability for IVD degeneration grading.

3 TECHNICAL EFFICACY STAGE: 1.