351-03-018 / 351-03-018 ISMRM Abstract

A Fraction of the Cost: Portable, Low-Cost and Energy-Efficient AI-Driven MRI analysis on Raspberry Pi and NVIDIA Jetson Nano

Primary: Analysis Methods - Segmentation and Detection

Secondary: Cardiovascular - Myocardium

351-03-018 · AI Methods · Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1

Keywords: Open-source software Deep Learning Segmentation Medical Image Segmentation Automatic reporting Low cost design

Accepted

Minh Nhat Trinh¹, Teresa M Correia ^1,2

¹Quantitative Bio-Imaging Lab, CCMAR, Faro, Portugal, Portugal

²School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Presenting Author: Teresa M Correia

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References

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8. Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng PA, et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Transactions on Medical Imaging. 2018; 37(11):2514–25. https://doi.org/10.1109/TMI.2018.2837502 [doi]

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10. Sofroniew N, Lambert T, Evans K, Nunez-Iglesias J, Bokota G, Winston P, et al. napari: a multi-dimensional image viewer for Python. Zenodo; 2022. https://doi.org/10.5281/zenodo.3555620 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/351-03-018