Automated quality assessment using appearance-based simulations and hippocampus segmentation on low-field paediatric brain MR images

TL;DR

This study develops and compares automated methods for quality assessment and hippocampal segmentation on low-field paediatric brain MRI images, addressing the lack of tools in low-resource settings.

Contribution

It introduces appearance-based simulations for quality assurance and evaluates atlas registration for hippocampal segmentation in low-field paediatric MRI.

Findings

DenseNet with artefact synthesis achieved 82.3% accuracy in quality assessment.

Atlas registration achieved a Dice score of 0.61 for hippocampal segmentation.

Results highlight challenges for detailed analysis using low-field MRI images.

Abstract

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses.