Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
462-05-012 ISMRM Abstract

Towards Robust and Efficient Landmark Tracking for Real-time Abdominal MR-guided Radiotherapy via Self-supervised Learning

Primary: Interventional - MR-Guided Radiotherapy
Secondary: Analysis Methods - Image Registration and Alignment
462-05-012 · Interventional MRI: Device, Catheter Tracking, and Image-Guided Therapy · Tuesday, 12 May, 4:00 PM–4:55 PM · Digital Posters Row C
Keywords: Image registration MR-Linac Self-supevised learning Abdominal MRI Landmark Tracking
Accepted
Pauline Ornela Megne Choudja 1, Marcel Nachbar2,3, Cihan Gani2, Marcel Büttner2, Daniela Thorwarth2,3, Thomas Küstner1
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany
2Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
3Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Germany
Presenting Author: Pauline Ornela Megne Choudja

Synopsis

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References

1. Schmidt MA, Payne GS. Radiotherapy planning using MRI. Physics in Medicine & Biology. 2015 Oct 28;60(22):R323. doi:10.1088/0031-9155/60/22/R323 [doi]
2. Keiper TD, Tai A, Chen X, Paulson E, Lathuilière F, Bériault S, Hébert F, Cooper DT, Lachaine M, Li XA. Feasibility of real‐time motion tracking using cine MRI during MR‐guided radiation therapy for abdominal targets. Medical physics. 2020 Aug;47(8):3554-66. doi: 10.1002/mp.14230 [doi]
3. Zhou SK, Greenspan H, Davatzikos C, Duncan JS, Van Ginneken B, Madabhushi A, Prince JL, Rueckert D, Summers RM. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. Proceedings of the IEEE. 2021 Feb 26;109(5):820-38. doi:10.1109/JPROC.2021.3054390 [doi]
4. Huttinga NR, Bruijnen T, Van Den Berg CA, Sbrizzi A. Real-time non-rigid 3D respiratory motion estimation for MR-guided radiotherapy using MR-MOTUS. IEEE Transactions on Medical Imaging. 2021 Sep 14;41(2):332-46. doi: 10.1109/TMI.2021.3112818 [doi]
5. Huttinga NR, Bruijnen T, van den Berg CA, Sbrizzi A. Gaussian Processes for real-time 3D motion and uncertainty estimation during MR-guided radiotherapy. Medical Image Analysis. 2023 Aug 1;88:102843. https://doi.org/10.1016/j.media.2023.102843 [doi]
6. Frueh M, Schilling A, Gatidis S, Kuestner T. Real time landmark detection for within-and cross subject tracking with minimal human supervision. IEEE Access. 2022 Aug 1;10:81192-202. doi:10.1109/ACCESS.2022.3195211 [doi]
7. Frueh M, Kuestner T, Nachbar M, Thorwarth D, Schilling A, Gatidis S. Self-supervised learning for automated anatomical tracking in medical image data with minimal human labeling effort. Computer Methods and Programs in Biomedicine. 2022 Oct 1;225:107085. https://doi.org/10.1016/j.cmpb.2022.107085 [doi]
8. Karaev N, Rocco I, Graham B, Neverova N, Vedaldi A, Rupprecht C. Cotracker: It is better to track together. InEuropean conference on computer vision 2024 Sep 29 (pp. 18-35). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-73033-7_2 [doi]
9. Tian L, Greer H, Kwitt R, Vialard FX, San José Estépar R, Bouix S, Rushmore R, Niethammer M. unigradicon: A foundation model for medical image registration. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2024 Oct 4 (pp. 749-760). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-72069-7_70 [doi]
10. Rokuss M, Kirchhoff Y, Akbal S, Kovacs B, Roy S, Ulrich C, Wald T, Rotkopf LT, Schlemmer HP, Maier-Hein K. LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging. In Proceedings of the Computer Vision and Pattern Recognition Conference 2025 (pp. 30872-30885). doi: 10.1109/CVPR52734.2025.02875 [doi]
11. Harley AW, Fang Z, Fragkiadaki K. Particle video revisited: Tracking through occlusions using point trajectories. In European Conference on Computer Vision 2022 Oct 23 (pp. 59-75). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-20047-2_4 [doi]
12. Yang D, Xiong T, Xu D, Zhou SK, Xu Z, Chen M, Park J, Grbic S, Tran TD, Chin SP, Metaxas D. Deep image-to-image recurrent network with shape basis learning for automatic vertebra labeling in large-scale 3D CT volumes. In International conference on medical image computing and computer-assisted intervention 2017 Sep 4 (pp. 498-506). Cham: Springer International Publishing. doi: 10.1007/978-3-319-66179-7_57 [doi]
13. Lian C, Wang F, Deng HH, Wang L, Xiao D, Kuang T, Lin HY, Gateno J, Shen SG, Yap PT, Xia JJ. Multi-task dynamic transformer network for concurrent bone segmentation and large-scale landmark localization with dental CBCT. In International Conference on Medical Image Computing and Computer-Assisted Intervention 2020 Sep 29 (pp. 807-816). Cham: Springer International Publishing. doi: 10.1007/978-3-030-59719-1_78 [doi]
14. Zhu H, Yao Q, Xiao L, Zhou SK. You only learn once: Universal anatomical landmark detection. In International conference on medical image computing and computer-assisted intervention 2021 Sep 21 (pp. 85-95). Cham: Springer International Publishing. doi: 10.48550/arXiv.2103.04657 [doi]
15. Teed Z, Deng J. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision 2020 Aug 23 (pp. 402-419). Cham: Springer International Publishing. https://doi.org/10.48550/arXiv.2003.12039 [doi]
16. Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. An unsupervised learning model for deformable medical image registration. In Proceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 9252-9260). https://doi.org/10.1109/CVPR.2018.00964 [doi]
17. Yang X, Kwitt R, Styner M, Niethammer M. Quicksilver: Fast predictive image registration–a deep learning approach. NeuroImage. 2017 Sep 1;158:378-96. https://doi.org/10.48550/arXiv.1703.10908 [doi]
18. De Vos BD, Berendsen FF, Viergever MA, Staring M, Išgum I. End-to-end unsupervised deformable image registration with a convolutional neural network. In International Workshop on Deep Learning in Medical Image Analysis 2017 Sep 9 (pp. 204-212). Cham: Springer International Publishing.
19. Pluim JP, Maintz JA, Viergever MA. Mutual-information-based registration of medical images: a survey. IEEE transactions on medical imaging. 2003 Jul 28;22(8):986-1004. doi: 10.1109/TMI.2003.815867 [doi]
20. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing. 2004 Apr 30;13(4):600-12. doi: 10.1109/TIP.2003.819861 [doi]
21. Godard C, Mac Aodha O, Brostow GJ. Unsupervised monocular depth estimation with left-right consistency. In Proceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 270-279). https://doi.org/10.48550/arXiv.1609.03677 [doi]
22. Wang Y, Yang Y, Yang Z, Zhao L, Wang P, Xu W. Occlusion aware unsupervised learning of optical flow. In Proceedings of the IEEE conference on computer vision and pattern recognition 2018 (pp. 4884-4893). doi: 10.1109/CVPR.2018.00513 [doi]
23. Tustison NJ, Cook PA, Holbrook AJ, Johnson HJ, Muschelli J, Devenyi GA, Duda JT, Das SR, Cullen NC, Gillen DL, Yassa MA. The ANTsX ecosystem for quantitative biological and medical imaging. Scientific reports. 2021 Apr 27;11(1):9068. doi: 10.1038/s41598-021-87564-6 [doi]

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http://echo.ismrm.org/p/ISMRM2026/462-05-012