Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-02-013 ISMRM Abstract

Self-Supervised Deep Learning for Label-Free Brain Metastasis Detection in Clinical MR Imaging

Primary: Analysis Methods - Segmentation and Detection
Secondary: Neuro - Tumors
364-02-013 · Innovations in Brain Tumor Imaging: Quantitative MRI, Radiogenomics, and Deep-Learning Approaches · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row E
Keywords: Deep learning Self-supevised learning Anomaly Detection Brain Metastasis
Accepted
Anne Rückert 1, Oscar van der Heide1, Mark Savenije1, Jelmer van Lune1, Niels C.P.J Raaijmakers2, Marielle Philippens2, Enrica Seravalli2, Mischa de Ridder2, Cornelis A van den Berg1
1Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, UMC Utrecht, Utrecht, Netherlands
2Department of Radiotherapy, UMC Utrecht, Utrecht, Netherlands
Presenting Author: Anne Rückert

Synopsis

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References

1. Takei H, Rouah E, Ishida Y. Brain metastasis: clinical characteristics, pathological findings and molecular subtyping for therapeutic implications. Brain Tumor Pathol 33, 1–12 (2016). https://doi.org/10.1007/s10014-015-0235-3. [doi]
2. Pekmezci M, Perry A. Neuropathology of brain metastases. Surg Neurol Int. 2013 May 2;4(Suppl 4):S245-55. PMID: 23717796; PMCID: PMC3656562. [pmid]
3. Grøvik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging. 2020 Jan;51(1):175-182. doi: 10.1002/jmri.26766. [doi]
4. Dikici E, Ryu JL, Demirer M, Bigelow M, White RD, Slone W, Erdal BS, Prevedello LM. Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI. IEEE J Biomed Health Inform. 2020 Oct;24(10):2883-2893. doi: 10.1109/JBHI.2020.2982103. [doi]
5. Ozkara BB, Chen MM, Federau C, Karabacak M, Briere TM, Li J, Wintermark M. Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis. Cancers (Basel). 2023 Jan 4;15(2):334. doi: 10.3390/cancers15020334. [doi]
6. Baugh M, Tan J, Müller JP, Dombrowski M, Batten J, Kainz B. (2023). Many Tasks Make Light Work: Learning to Localise Medical Anomalies from Multiple Synthetic Tasks. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_16. [doi]
7. Human Connectome Project (HCP) Young Adult Study. https://www.humanconnectome.org/study/hcp-young-adult. Accessed October 29, 2025.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-02-013