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Multilabel SegSRGAN-A framework for parcellation and morphometry of preterm brain in MRI.

Guillaume Dollé1, Gauthier Loron2,3, Margaux Alloux3,4

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This study introduces an enhanced SegSRGAN method for multi-label brain MRI segmentation in newborns. A quality control protocol ensures accurate neurodevelopmental biomarkers for preterm infants.

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Area of Science:

  • Medical Imaging
  • Neuroscience
  • Biomedical Engineering

Background:

  • Neonatal Magnetic Resonance Imaging (MRI) is crucial for assessing neurodevelopmental issues in premature infants.
  • Clinical MRI data presents challenges due to inherent image properties and acquisition variability, necessitating robust segmentation methods.
  • Accurate segmentation is vital for deriving reliable biomarkers from neonatal brain MR images.

Purpose of the Study:

  • To extend the SegSRGAN method for multi-label segmentation of neonatal brain MRI.
  • To develop and validate a quality control protocol for assessing segmentation accuracy in this context.
  • To evaluate SegSRGAN's suitability for clinical research in neonatal brain morphometry and biomarker discovery.

Main Methods:

  • Extension of SegSRGAN for multi-label segmentation, partitioning MR images into specific brain tissue/area labels.
  • Development of a quality control protocol combining expert analysis, morphometric measurements, and topological properties.
  • Application of the method and protocol to the EPIRMEX neonatal MRI dataset.

Main Results:

  • Successful implementation of multi-label SegSRGAN for neonatal brain parcellation.
  • Validation of the proposed quality control protocol for segmentation performance assessment.
  • Investigation into the strengths and weaknesses of SegSRGAN for clinical morphometric analysis.

Conclusions:

  • The enhanced SegSRGAN and its quality control protocol show promise for accurate neonatal brain segmentation.
  • This work is a foundational step towards 3D neonatal brain morphometry and the development of novel neurodevelopmental biomarkers.
  • The open-source code facilitates further research in preterm infant brain analysis.