Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
368-02-006 ISMRM Abstract

Automated Multi-Tissue Knee Segmentation from Low-Resolution Dynamic UTE MRI using 3D U-Net

Primary: Analysis Methods - Segmentation and Detection
Secondary: Musculoskeletal - Functional/Dynamic
368-02-006 · Segmentation for Musculoskeletal MRI Applications · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row I
Keywords: Deep Learning Segmentation 3D U-Net Dynamic UTE Knee MRI MONAI
Accepted
Aayush Nepal 1, Nicholas M Brisson2,3, Jürgen R Reichenbach1, Martin Krämer4
1Medical Physics Group, Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
2Julius Wolff Institute, Berlin, Germany
3Berlin Institute of Health at Charité, Julius Wolff Institute, Berlin, Germany
4Institute of Diagnostic and Interventional Radiology, Jena University Hospital, Jena, Germany
Presenting Author: Aayush Nepal

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References

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2. Tang X, Guo D, Liu A, et al. Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model. Med Sci Monit. 2022;28:e936733. doi:10.12659/MSM.936733 [doi]
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7. Brisson NM, Krämer M, Krahl LAN, Schill A, Duda GN, Reichenbach JR. A novel multipurpose device for guided knee motion and loading during dynamic magnetic resonance imaging. Zeitschrift für Medizinische Physik. 2022;32(4):500-513. doi:10.1016/j.zemedi.2021.12.002 [doi]
8. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Vol 9901. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2016:424-432. doi:10.1007/978-3-319-46723-8_49 [doi]
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Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/368-02-006