Abstract #2096

A-Eye: quality control and deep learning segmentation of the complete eye in MRI

Jaime Barranco^1,2,3, Hamza Kebiri^1,2,3, Óscar Esteban², Raphael Sznitman⁴, Sönke Langner^5,6, Oliver Stachs⁷, Adrian Konstantin Luyken⁷, Philipp Stachs⁸, Benedetta Franceschiello^2,3,9,10,11, and Meritxell Bach Cuadra^3,11

¹Center for Biomedical Imaging (CIBM), Lausanne, Switzerland, ²Lausanne University Hospital (CHUV ), Lausanne, Switzerland, ³University of Lausanne (UNIL), Lausanne, Switzerland, ⁴ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland, ⁵Institute for Diagnostic and Interventional Radiology, Pediatric and Neuroradiology, Rostock University Medical Center, Rostock, Germany, ⁶Department of Diagnostic Radiology and Neuroradiology, University of Greifswald, Greifswald, Germany, ⁷Department of Ophthalmology, Rostock University Medical Center, Rostock, Germany, ⁸Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany, ⁹HES-SO Valais-Wallis, Sion, Switzerland, ¹⁰The Sense Innovation and Research Center, Sion and Lausanne, Switzerland, ¹¹These authors provided equal last-authorship contribution, Lausanne, Switzerland

Synopsis

Keywords: Analysis/Processing, Segmentation, Quality Assessment and Control, Eye, MREye, Ophthalmology, Ocular

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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