Fourier-inspired Automatic Cobb Angles Measurement for Adolescent Idiopathic Scoliosis

Fourier-inspired Automatic Cobb Angles Measurement for Adolescent Idiopathic Scoliosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Fourier-inspired Automatic Cobb Angles Measurement for Adolescent Idiopathic Scoliosis Yishan Zhong, Felipe Giuste, Wenqi Shi, Ben Tamo, Micky Nnamdi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6010694/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 24 You are reading this latest preprint version Abstract Measuring Cobb angles in adolescent idiopathic scoliosis (AIS) patients using anteroposterior (AP) radiographs is time-consuming and prone to variability due to subjective vertebrae selection. To address this, we propose an automated model for Cobb angle measurement using a Fourier-inspired method. The model starts by estimating the spinal curvature spectrum and then predicts the Cobb angles. We use a VGG-16 transfer learning model to regress the concatenated real and imaginary components of the spine's spectral representation. This feature is used to reconstruct the curvature through an inverse Fourier transform for improved model explainability. Simultaneously, it serves as input to a projection head for Cobb angle estimation. Evaluating on the MICCAI 2019 SpineWeb dataset, our model shows a Pearson correlation of 0.9382 for curvature estimation and a symmetric mean absolute percentage error (SMAPE) of 12.25% for Cobb angle measurement, performing similarly to other top models. By treating spinal curvature prediction as a frequency domain analysis, our method offers more accurate curvature shapes than spatial domain approaches. Additionally, applying zero padding improves spectral resolution and reduces SMAPE in Cobb angle estimation. Spectral prediction consistently outperforms spatial prediction in both curvature and Cobb angle measurement. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 29 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviews received at journal 06 Aug, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Editor invited by journal 03 Aug, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 15 Jun, 2025 Reviewers agreed at journal 14 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 21 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 09 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6010694","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":446085438,"identity":"234f5dcb-39ad-4d1f-82a5-2f1eae6d4624","order_by":0,"name":"Yishan Zhong","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yishan","middleName":"","lastName":"Zhong","suffix":""},{"id":446085442,"identity":"63f5c7c4-690a-4ef3-9094-50e12013d686","order_by":1,"name":"Felipe Giuste","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"","lastName":"Giuste","suffix":""},{"id":446085445,"identity":"1e244c27-94a7-4811-aab9-dce78274914f","order_by":2,"name":"Wenqi Shi","email":"","orcid":"","institution":"The University of Texas Southwestern Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Wenqi","middleName":"","lastName":"Shi","suffix":""},{"id":446085446,"identity":"14d7cd87-e689-4908-9048-04b71203245d","order_by":3,"name":"Ben Tamo","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ben","middleName":"","lastName":"Tamo","suffix":""},{"id":446085449,"identity":"5e097e2b-a346-4ac2-87da-4f2171b91ce8","order_by":4,"name":"Micky Nnamdi","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Micky","middleName":"","lastName":"Nnamdi","suffix":""},{"id":446085457,"identity":"8548eed7-ead5-4550-9bac-2a3f1ebdb45a","order_by":5,"name":"Benoit L Marteau","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Benoit","middleName":"L","lastName":"Marteau","suffix":""},{"id":446085458,"identity":"eeb32310-80a5-4e73-86ea-6303bee11ef1","order_by":6,"name":"Kaiwen Wang","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Kaiwen","middleName":"","lastName":"Wang","suffix":""},{"id":446085459,"identity":"bb11ff5a-7c3e-407f-8ba9-b258608dd28e","order_by":7,"name":"Steven W Hwang","email":"","orcid":"","institution":"Shriners Hospitals for Children - 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To address this, we propose an automated model for Cobb angle measurement using a Fourier-inspired method. The model starts by estimating the spinal curvature spectrum and then predicts the Cobb angles. We use a VGG-16 transfer learning model to regress the concatenated real and imaginary components of the spine's spectral representation. This feature is used to reconstruct the curvature through an inverse Fourier transform for improved model explainability. Simultaneously, it serves as input to a projection head for Cobb angle estimation. Evaluating on the MICCAI 2019 SpineWeb dataset, our model shows a Pearson correlation of 0.9382 for curvature estimation and a symmetric mean absolute percentage error (SMAPE) of 12.25% for Cobb angle measurement, performing similarly to other top models. By treating spinal curvature prediction as a frequency domain analysis, our method offers more accurate curvature shapes than spatial domain approaches. 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