Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-03-009 ISMRM Abstract

An nnU-Net Model for the Whole Orbit Structures Segmentation in Thyroid Eye Disease Patients

Primary: Analysis Methods - Segmentation and Detection
Secondary: Neuro - Head & Neck/ENT
664-03-009 · Innovations in Head and Neck Imaging: From Coil Design to Clinical Impact · Thursday, 14 May, 1:40 PM–2:35 PM · Digital Posters Row E
Keywords: Segmentation Deep learning Orbital MRI Thyroid Eye Disease
Accepted
Yuanyuan Cui 1, Yunmeng Wang1, Wenhao Jia2, Jiankun Dai3, Li Fan1, Shiyuan Liu1, Yi Xiao1
1Department of Radiology, The Second Affiliated Hospital of Naval Medical University, SHANGHAI, China
2Shanghai Jiuzhou Intelligent Medical Imaging Institute, Shanghai, China
3MR Research, Beijing, China
Presenting Author: Yuanyuan Cui

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References

1. Bahn RS. Graves' ophthalmopathy. N Engl J Med. 2010;362(8):726-738. doi:10.1056/NEJMra0905750. [doi]
2. Bahn RS. Current Insights into the Pathogenesis of Graves' Ophthalmopathy. Horm Metab Res. 2015;47(10):773-778. doi:10.1055/s-0035-1555762. [doi]
3. Burch HB, Perros P, Bednarczuk T, et al. Management of Thyroid Eye Disease: A Consensus Statement by the American Thyroid Association and the European Thyroid Association. Thyroid. 2022;32(12):1439-1470. doi:10.1089/thy.2022.0251. [doi]
4. Barrio-Barrio J, Sabater AL, Bonet-Farriol E, Velázquez-Villoria Á, Galofré JC. Graves' Ophthalmopathy: VISA versus EUGOGO Classification, Assessment, and Management. J Ophthalmol. 2015;2015:249125. doi:10.1155/2015/249125. [doi]
5. Wang Y, Cui Y, Dai J, et al. Prospective Comparison of FOCUS MUSE and Single-Shot Echo-Planar Imaging for Diffusion-Weighted Imaging in Evaluating Thyroid-Associated Ophthalmopathy. Korean J Radiol. 2024;25(10):913-923. doi:10.3348/kjr.2024.0177. [doi]
6. Wang Y, Cui Y, Cheng Y, et al. Rapid multiparametric quantitative MRI for predicting the activity of thyroid-associated ophthalmopathy: combination with clinical characteristics. Eur Radiol. Published online June 9, 2025. doi:10.1007/s00330-025-11691-1. [doi]
7. Zhang H, Jiang M, Chan HC, et al. Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease. J Transl Med. 2024;22(1):56. Published 2024 Jan 13. doi:10.1186/s12967-023-04792-2. [doi]
8. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211. doi:10.1038/s41592-020-01008-z. [doi]
9. Zhang K, Liu C, Pan J, et al. Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis. Eur J Radiol. 2024;172:111347. doi:10.1016/j.ejrad.2024.111347 [doi]
10. Wang G, Yang B, Qu X, et al. Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study. Neuroradiology. 2024;66(10):1781-1791. doi:10.1007/s00234-024-03429-5 [doi]
11. Alkhadrawi AM, Lin LY, Langarica SA, et al. Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease. Invest Ophthalmol Vis Sci. 2024;65(5):6. doi:10.1167/iovs.65.5.6. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/664-03-009