{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:19:12Z","timestamp":1768569552622,"version":"3.49.0"},"posted":{"date-parts":[[2025,3,4]]},"group-title":"Orthopedics","reference-count":39,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T00:00:00Z","timestamp":1741046400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,3,4]]},"abstract":"<jats:sec>\n                  <jats:title>Purpose<\/jats:title>\n                  <jats:p>Low back pain is the world\u2019s 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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Our segmentation model achieved an IoU of 0.82 (95% CI: 0.80\u20130.84) and DSC of 0.90 (95% CI: 0.89\u20130.91). The minimum bottleneck size for which the CNN autoencoder converged was 4\u00d71 after 350 epochs (IoU of 0.9984 - 95% CI: 0.9979\u20130.9989). 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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2025.02.28.25323111","type":"posted-content","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T23:30:14Z","timestamp":1741131014000},"source":"Crossref","is-referenced-by-count":0,"title":["A CNN Autoencoder for Learning Latent Disc Geometry from Segmented Lumbar Spine MRI"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7062-4984","authenticated-orcid":false,"given":"Mattia","family":"Perrone","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D\u2019Mar","family":"Moore","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daisuke","family":"Ukeba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John T.","family":"Martin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025030704072934000_2025.02.28.25323111v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/S2665-9913(23)00098-X"},{"key":"2025030704072934000_2025.02.28.25323111v1.2","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0b013e3181aa5b33"},{"issue":"17","key":"2025030704072934000_2025.02.28.25323111v1.3","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1097\/00007632-200109010-00011","article-title":"Magnetic resonance classification of lumbar intervertebral disc degeneration","volume":"26","year":"2001","journal-title":"Spine (Phila Pa 1976)"},{"key":"2025030704072934000_2025.02.28.25323111v1.4","doi-asserted-by":"crossref","unstructured":"Perrone M , Guidetti M , Galli M , Nho SJ , Wimmer MA , Malloy P. 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