551-02-003 / 551-02-003 ISMRM Abstract

Parenchymal Tissue Segmentation in a Multi-Vendor Chronic Kidney Disease Cohort

Primary: Body - Kidney

Secondary: Analysis Methods - Segmentation and Detection

551-02-003 · MRI of Renal Function · Wednesday, 13 May, 1:40 PM–3:16 PM · Power Pitch Theatre 1

Keywords: Kidney Machine Learning/Artificial Intelligence Segmentation Chronic Kidney Disease Renal

Accepted

Alexander J Daniel ¹, Charlotte E Buchanan¹, James P Stowe¹, Eleanor F Cox^1,2, Andrew N Priest³, David L Thomas^4,5,6, Mark Gilthorpe⁷, Philip Kalra⁸, Iosif A Mendichovszky⁹, Steven Sourbron¹⁰, Maarten Taal¹¹, Nicholas Selby¹¹, Susan Francis^1,2

¹Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom

²NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, United Kingdom

³Department of Radiology, University of Cambridge, Cambridge, United Kingdom

⁴Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

⁵Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

⁶Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom

⁷Obesity Institute, Leeds Beckett University, Leeds, United Kingdom

⁸Salford Royal NHS Foundataion Trust, Salford, United Kingdom

⁹Department of Radiology, Addenbrooke's Hospital, Cambridge, United Kingdom

¹⁰Department of Infection, Immunity and Cardiovascular Disease, The University of Sheffield, Sheffield, United Kingdom

¹¹Centre for Kidney Research and Innovation, University of Nottingham, Nottingham, United Kingdom

Presenting Author: Alexander J Daniel

Synopsis

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References

1. Qualification of Biomarker Total Kidney Volume in Studies for Treatment of Autosomal Dominant Polycystic Kidney Disease Draft Guidance for Industry. U.S. Food and Drug Administration. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/qualification-biomarker-total-kidney-volume-studies-treatment-autosomal-dominant-polycystic-kidney. Published September 16, 2016. Accessed October 21, 2025.

2. Daniel AJ, Buchanan CE, Allcock T, et al. Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network. Magn Reson Med. 2021;86(2):1125-1136. https://doi.org/10.1002/mrm.28768 [doi]

3. Daniel AJ, Buchanan C, Morris DM, et al. Generalisability of Automated CNN-based Renal Segmentation for Multi-Vendor Studies. In: Proc. Intl. Soc. Mag. Reson. Med. 31. London; 2022:2160. https://doi.org/10.58530/2022/2160 [doi]

4. Francis ST, Buchanan CE, Alexander J Daniel, et al. The Application of FunctIonal Renal MRI to improve assessment of Chronic Kidney Disease (AFiRM) Study. In: Proc. Intl. Soc. Mag. Reson. Med. 33. Singapore; 2024:0779.

5. Buchanan CE, Li H, Nery F, et al. Harmonisation of Multiparametric Renal MRI for Multi-Centre Studies. In: Proc. Intl. Soc. Mag. Reson. Med. 30. Vol 29. Online; 2021:0417.

6. Will S, Martirosian P, Würslin C, Schick F. Automated segmentation and volumetric analysis of renal cortex, medulla, and pelvis based on non-contrast-enhanced T1- and T2-weighted MR images. Magn Reson Mater Phys Biol Med. 2014;27(5):445-454. doi:10.1007/s10334-014-0429-4 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/551-02-003