Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
564-06-013 ISMRM Abstract

Deep Learning for Automated Meningioma Segmentation: Toward Clinical Integration and Workflow Efficiency

Primary: Neuro - Tumors
Secondary: Analysis Methods - Segmentation and Detection
564-06-013 · Segmentation for Neuro Applications · Wednesday, 13 May, 4:55 PM–5:50 PM · Digital Posters Row E
Keywords: Segmentation Meningiomas Clinical utility
Accepted
Laxmi Muralidharan1, James K Ruffle2,3, Ebru Fenney4, Anand Pandit1, Hani Marcus4, Parashkev Nachev4, Harpreet Hyare 4
1University College London, London, United Kingdom
2Queen Square Institute of Neurology, University College London, London, United Kingdom
3Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
4Department of Translational Neuroscience and Stroke, University College London, London, United Kingdom
Presenting Author: Harpreet Hyare

Synopsis

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References

1. Laukamp KR et al. Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI. Eur Radiol. 2019;29(1):124–132. PMID: 29943184. doi:10.1007/s00330-018-5595-8 [doi] [pmid]
2. Laukamp KR et al. Automated meningioma segmentation in multiparametric MRI: Comparable effectiveness of a deep learning model and manual segmentation. Clin Neuroradiol. 2021;31(2):357–366. PMID: 32060575. doi:10.1007/s00062-020-00884-4 [doi] [pmid]
3. LaBella DJ et al. A multi-institutional meningioma MRI dataset for automated segmentation. Sci Data. 2024;11: Article 33 350. doi:10.1038/s41597-024-03350-9 [doi]
4. Chen C et al. Automatic meningioma segmentation and grading prediction: A hybrid deep-learning method. J Pers Med. 2021;11(8):786. doi:10.3390/jpm11080786 [doi]
5. Wang T-W et al. Performance of convolutional neural network models in meningioma segmentation in MRI: A systematic review and meta-analysis. Neuroinformatics. 2024. doi:10.1007/s12021-024-09704-3 [doi]
6. 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;18:203–211. PMID: 33288961. doi:10.1038/s41592-020-01008-z [doi] [pmid]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/564-06-013