Automatic brain segmentation in preterm infants with post-hemorrhagic hydrocephalus using 3D Bayesian U-Net.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neonatal Neurology

Background:

• Post-hemorrhagic hydrocephalus (PHH) is a severe complication in very preterm infants following intraventricular hemorrhage (IVH).
• Current PHH monitoring relies on subjective 2D measurements, potentially limiting accurate assessment and treatment decisions.
• Accurate 3D ventricular measurements are crucial for improved PHH management.

Purpose of the Study:

• To develop and evaluate a Bayesian U-Net method for automatic 3D brain segmentation in preterm infants with PHH.
• To assess the segmentation accuracy and uncertainty quantification of the proposed Bayesian U-Net.
• To compare the Bayesian U-Net against DenseNet, U-Net, and ensemble learning methods.

Main Methods:

• Utilized 41 T2-weighted MRIs from 27 preterm infants with PHH.
• Manually segmented brain structures (ventricles, CSF, gray/white matter, brainstem, cerebellum) to create ground truth data.
• Employed 4-fold cross-validation to train and evaluate the Bayesian U-Net, DenseNet, U-Net, and ensemble learning models.

Main Results:

• The Bayesian U-Net achieved a superior mean Dice score of 0.948 ± 0.034 for lateral ventricle segmentation, outperforming other methods.
• The Bayesian U-Net demonstrated strong uncertainty quantification with a mean recall of 0.953 ± 0.037 and mean AUC of 0.949 ± 0.031.
• Segmentation accuracy for the Bayesian U-Net was high across various brain structures.

Conclusions:

• The Bayesian U-Net method provides accurate and reliable automatic brain segmentation for preterm infants with PHH.
• This AI-driven approach offers valuable uncertainty assessment, potentially enhancing clinical decision-making.
• The developed method holds promise for improving PHH monitoring and treatment strategies in neonatal care.