AUTHORS: Barranco J., Kebiri H., Esteban Ó., Sznitman R., Langner S., Stachs O., Luyken A. K., Stachs P., Franceschiello B., Bach Cuadra M.

International Society for Magnetic Resonance in Medicine, : , Singapore, May 2024

ABSTRACT

Motivation:

Reliable large-scale MREye segmentation.

Goal(s):

Quality control of eye MRI and deep learning segmentation validation.

Approach:

We automatically extract Image Quality Metrics (IQMs) and use them as features to train a model in a supervised framework with expert rating annotations as target. Multi-class 3D MREye segmentation is done for the first time using the deep-learning-based approach nnUNet.

Results:

None of the models achieved the required levels of sensitivity and specificity necessary for our MREye application. nnUNet for MREye segmentation tasks yielded promising outcomes, robust to a variety of MRI quality.

Impact:

MREye does not escape the evidence that insufficient data quality threatens the reliability of analysis outcomes. We pioneer manual and automated quality control on MREye and benchmark deep learning eye segmentation.

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