Purpose Low back pain is the world’s leading cause of disability and pathology of the lumbar intervertebral discs is frequently considered a driver of pain. The geometric characteristics of intervertebral discs offer valuable insights into their mechanical behavior and pathological conditions. In this study, we present a convolutional neural network (CNN) autoencoder to extract latent features from segmented disc MRI. Additionally, we interpret these latent features and demonstrate their utility in identifying disc pathology, providing a complementary perspective to standard geometric measures.

Methods We examined 195 sagittal T1-weighted MRI of the lumbar spine from a publicly available multi-institutional dataset. The proposed pipeline includes five main steps: 1) segmenting MRI, 2) training the CNN autoencoder and extracting latent geometric features, 3) measuring standard geometric features, 4) predicting disc narrowing with latent and/or standard geometric features and 5) determining the relationship between latent and standard geometric features.

Results Our segmentation model achieved an IoU of 0.82 (95% CI: 0.80–0.84) and DSC of 0.90 (95% CI: 0.89–0.91). The minimum bottleneck size for which the CNN autoencoder converged was 4×1 after 350 epochs (IoU of 0.9984 - 95% CI: 0.9979–0.9989). Combining latent and geometric features improved predictions of disc narrowing compared to using either feature set alone. Latent geometric features encoded for disc shape and angular orientation.

Conclusions This study presents a CNN-autoencoder to extract latent features from segmented lumbar disc MRI, enhancing disc narrowing prediction and feature interpretability. Future work will integrate disc voxel intensity to analyze composition.

The authors have declared no competing interest.

This study did not receive any funding

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The study used only openly available human data that were originally located at https://zenodo.org/records/8009680

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All data produced in the present study are available upon reasonable request to the authors