A CNN Autoencoder for Learning Latent Disc Geometry from Segmented Lumbar Spine MRI

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.

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.

Our segmentation model achieved an intersection over union (IoU) of 0.82 (95% CI 0.80-0.84) and dice similarity coefficient (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 use either feature set alone. Latent geometric features encoded for disc shape and angular orientation.

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.