303-01-008 ISMRM Abstract

Transfer learning–based segmentation of paediatric optic pathway gliomas with incomplete MRI

Primary: Pediatrics - Neuro

Secondary: Analysis Methods - Segmentation and Detection

303-01-008 · AI Methods Beyond Reconstruction · Monday, 11 May, 8:20 AM–10:10 AM · Auditorium 1

Keywords: Machine Learning/Artificial Intelligence Brain Tumor Segmentation Deep Learning Segmentation Paediatric neuroimaging Optic Pathway Glioma (OPG)

Accepted

Xinyu Tian ¹, Enrico De Vita^2,3,4, Kshitij Mankad⁵, Emily R Drabek-Maunder⁶, James K Ruffle⁷, Chris A Clark⁸

¹Department of Computer Science, University College London, London, United Kingdom

²MR Physics Group. Radiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom

³Developmental Imaging and Biophysics Unit. Department of Developmental Neuroscience, UCL Great Ormond Street Institute of Child Health, London, United Kingdom

⁴Great Ormond Street Hospital for Children NHS Foundation Trust, London, United Kingdom

⁵Radiology, Great Ormond Street Hospital, London, United Kingdom

⁶UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

⁷Queen Square Institute of Neurology, University College London, London, United Kingdom

⁸Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

Presenting Author: Xinyu Tian

Synopsis

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References

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2. J. Nalepa, S. Adamski, K. Kotowski, et al., Segmenting pediatric optic pathway gliomas from mri using deep learning, Computers in Biology and Medicine 142 (2022) 105237. doi: 10.1016/j.compbiomed.2022.105237 [doi]

3. J. K. Ruffle, S. Mohinta, R. Gray, H. Hyare, P. Nachev, Brain tumour segmentation with incomplete imaging data, Brain Communications 5 (2) (2023) fcad118. doi: 10.1093/braincomms/fcad118 [doi]

4. U. Baid, S. Ghodasara, S. Mohan, et al., The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification, arXiv preprint arXiv:2107.02314 (2021). doi: 10.48550/arXiv.2107.02314 [doi]

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7. A. Fathi Kazerooni, N. Khalili, X. Liu, et al., Brats-peds: Results of the multi-consortium international pediatric brain tumor segmentation challenge 2023, Machine Learning for Biomedical Imaging 3 (June 2025) (2025) 72–87. doi:10.59275/j.melba.2025-f6fg [doi]

8. L. Weizman, L. Joskowicz, L. Ben-Sira, R. Precel, D. Ben-Bashat, Automatic segmentation of optic pathway gliomas in mri, in: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, 2010, pp. 920–923. doi: 10.1007/978-3-642-15705-9_13 [doi]

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11. A. Mansoor, I. Li, R. J. Packer, R. A. Avery, M. G. Linguraru, Joint deep shape and appearance learning: application to optic pathway glioma segmentation, in: Medical Imaging 2017: Computer-Aided Diagnosis, Vol. 10134, SPIE, 2017, pp. 423–429. doi: 10.1117/12.2255580 [doi]

12. M. Artzi, S. Gershov, L. Ben-Sira, J. Roth, D. Kozyrev, B. Shofty, T. Gazit, T. Halag-Milo, S. Constantini, D. Ben Bashat, Automatic segmentation, classification, and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on mri, Medical Physics 47 (11) (2020) 5693–5701. doi: 10.1002/mp.14489 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/303-01-008