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Deep learning-based, fully automated, pediatric brain segmentation.

Min-Jee Kim1, EunPyeong Hong2, Mi-Sun Yum3

  • 1Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.

Scientific Reports
|February 21, 2024
PubMed
Summary

A novel deep learning-based brain segmentation (DLS) method accurately measures brain volumes in children. This automated DLS approach is comparable to traditional methods and effectively detects brain changes in neurodevelopmental disorders.

Keywords:
Convolutional neural networkDeep learning-based segmentationDravet syndromeVUNO Med-DeepBrain

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Area of Science:

  • Neuroimaging
  • Pediatric Neurology
  • Artificial Intelligence in Medicine

Background:

  • Accurate brain volumetry is crucial for understanding neurodevelopmental disorders.
  • Traditional methods like Freesurfer with manual correction are time-consuming and require expertise.
  • Automated segmentation tools are needed to improve efficiency and consistency in pediatric neuroimaging.

Purpose of the Study:

  • To evaluate the performance of a fully automated deep learning-based brain segmentation (DLS) method.
  • To compare DLS-derived brain volumes with those obtained from Freesurfer with manual correction in children.
  • To assess the DLS method's ability to detect brain volume differences in pediatric patients with SCN1A mutations.

Main Methods:

  • A deep learning-based brain segmentation (DLS) method was employed for automated analysis.
  • Brain volumes (whole, cortical, subcortical) were analyzed in 21 pediatric patients with SCN1A mutations and 42 healthy controls.
  • DLS results were compared against volumes measured using Freesurfer with manual correction.

Main Results:

  • DLS showed high consistency with Freesurfer for total gray and white matter volumes in healthy controls.
  • Minor differences were observed in 7 out of 68 cortical parcellated volumes, decreasing with age.
  • The DLS method successfully identified reduced brain volumes in SCN1A mutation patients compared to controls.

Conclusions:

  • The automated DLS method is a reliable and compatible alternative to Freesurfer with manual correction for pediatric brain volumetry.
  • DLS effectively detects brain morphological changes in children with neurodevelopmental disorders.
  • This automated approach offers a promising tool for efficient and accurate analysis in pediatric neuroimaging research.