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

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 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.

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.