Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
464-05-006 ISMRM Abstract

Attention-Enhanced Few-Shot 3D U-Net for Infant Brain MRI Segmentation

Primary: Analysis Methods - Segmentation and Detection
Secondary: Pediatrics - Neuro
464-05-006 · MR-Based Quantitative Image and Image Processing · Tuesday, 12 May, 4:00 PM–4:55 PM · Digital Posters Row E
Keywords: Infant brain MRI Few-shot learning 3D U-Net Attention mechanism Paediatric neuroimaging
Accepted
Zaheer Abbas 1, Faizan ULLAH1, Sergo Gegechkori1, N. Jon Shah1,2,3,4
1Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany
2Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Germany
3JARA - BRAIN - Translational Medicine, Aachen, Germany
4Department of Neurology, RWTH Aachen University, Aachen, Germany
Presenting Author: Zaheer Abbas

Synopsis

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References

1. Esteban O, et al. Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines. Sci Data. 2019.
2. Roy AG, et al. Concurrent spatial and channel squeeze & excitation in fully convolutional networks. MICCAI. 2018.
3. Ronneberger O, et al. U-Net: Convolutional networks for biomedical image segmentation. MICCAI. 2015.
4. Wang L, et al. Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge. IEEE Trans Med Imaging. 2019.
5. Sun Y, et al. Multi-site infant brain segmentation algorithms: the iSeg-2019 challenge. IEEE Trans Med Imaging. 2021.
6. Ouyang C, et al. Self-supervised learning for few-shot medical image segmentation. IEEE Trans Med Imaging. 2022.
7. Finn C, et al. Model-agnostic meta-learning for fast adaptation of deep networks. ICML. 2017.
8. Tran AT, Zeevi T, Payabvash S. Strategies to improve robustness of deep learning segmentation. BioMedInformatics. 2025.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/464-05-006