Abstract #0482

Propagating Segmentation Uncertainties in Ultra-Low Field Using Matched Scans and Bootstrapping

Aksel Leknes^1,2, Ayo Zahra¹, Daniel Elijah Scheiene¹, Russel Macleod^3,4, Chiara Casella^3,4, James H Cole^5,6, Jonathan O'Muircheartaigh^3,4,7, Victoria Nankabirwa^8,9, and Muriel Bruchhage^1,10

¹Institute of Social Sciences, University of Stavanger, Stavanger, Norway, ²Developmental Imaging, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Australia, ³Department of Forensic & Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom, ⁴Research Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ⁵Centre for Medical Image Computing, University College London, London, United Kingdom, ⁶Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁷MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom, ⁸School of Public Health, Makerere University, Kampala, Uganda, ⁹Centre for Intervention Science in Maternal and Child Health, University of Bergen, Bergen, Norway, ¹⁰Stavanger Medical Imaging Laboratory, Department of Radiology, Stavanger University Hospital, Stavanger, Norway

Synopsis

Keywords: Low-Field MRI, Brain

Motivation: Portable ultra-low field (ULF) MRI scanning allows for broader and more accessible scanning in a multitude of settings – including rural areas and low- and middle-income countries. However, most normative brain models assessing typical and atypical neurodevelopment are based on high field MRI results using point estimates for segmentation predictions, not accounting for intrinsic uncertainty in ULF pediatric neuroimaging.

Goal(s): To provide model and brain-region specific uncertainty estimates for segmentation volumes.

Approach: Using bootstrapping to propagate model uncertainties onto segmentation outputs.

Results: Bootstrapping estimates revealed significant differences between high field and ULF segmentation estimates which can impact clinical decision-making.

Impact: Robust uncertainty estimates for brain anatomy are fundamental for investigating typical and atypical neurodevelopment. Propagating model uncertainties to volumetric estimates by comparing ULF results against gold standard high-field MRI can help guide diagnosis and assessment of brain health.

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