Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
562-03-007 ISMRM Abstract

Vascular Atrophy and Lesion Segmentation in Stroke: Extending Multi-U-Net LST-AI

Primary: Analysis Methods - Segmentation and Detection
Secondary: Neuro - Stroke and Cerebrovascular Disorders
562-03-007 · Machine Learning in Neuroimaging · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row C
Keywords: Stroke Artificial Intelligence White matter hyperintensity Deep Learning Segmentation Lesion Detection
Accepted
S Senthil Kumaran1, Himanshu Singh 1, Yug Kaushik2, Chirag Sharma2, Vishnu V. Y.3, Leve Joseph Devarajan Sebastian4, Ajay Garg4
1Department of Nuclear Magnetic Resonance (NMR), All India Institute of Medical Sciences, New Delhi, India
2University School of Automation & Robotics (USAR), GGSIPU, New Delhi, India
3Department of Neurology, All India Institute of Medical Sciences, New Delhi, India
4Department of Neuroimaging & Interventional Neuroradiology, All India Institute of Medical Sciences, New Delhi, India
Presenting Author: Himanshu Singh

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References

1. 1. Rondinella, A. et al. Boosting multiple sclerosis lesion segmentation through attention mechanism. Comput. Biol. Med. 161, 107021 (2023). https://doi.org/10.1016/j.compbiomed.2023.107021 [doi]
2. 2. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Med. Image Comput. Comput. Assist. Interv. 9351, 234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
3. 3. Gebre, R. K. et al. Cross–scanner harmonization methods for structural MRI may need further work: A comparison study. NeuroImage 269, 119912 (2023). https://doi.org/10.1016/j.neuroimage.2023.119912 [doi]
4. 4. Ming, Y. et al. Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: A systematic review. Front. Neurosci. 18, 1401329 (2024). https://doi.org/10.3389/fnins.2024.1401329 [doi]
5. 5. Menze, B. H. et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694 [doi]
6. 6. Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A. & Gee, J. C. A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011). https://doi.org/10.1016/j.neuroimage.2010.09.025 [doi]
7. 7. Schmidt, P. et al. An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis. NeuroImage 59, 3774–3783 (2012). https://doi.org/10.1016/j.neuroimage.2011.11.032 [doi]
8. 8. Grefkes, C., Fink, G.R. Recovery from stroke: current concepts and future perspectives.Neurol. Res. Pract. 2, 17 (2020). https://doi.org/10.1186/s42466-020-00060-6 [doi]
9. 9. Carmichael, S. T. The 3 Rs of stroke biology: Radial, relayed, and regenerative. Neurotherapeutics 13, 348–359 (2016). https://doi.org/10.1007/s13311-015-0408-0 [doi]
10. 10. Gwak, DS., Ryu, WS., Schellingerhout, D. et al. Effects of white matter hyperintensity burden on functional outcome after mild versus moderate-to-severe ischemic stroke. Sci Rep 14, 22567 (2024). https://doi.org/10.1038/s41598-024-71936-9 [doi]

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http://echo.ismrm.org/p/ISMRM2026/562-03-007