VINNA for neonates: Orientation independence through latent augmentations

Area of Science:

• Medical Imaging
• Neuroscience
• Artificial Intelligence

Background:

• Neonatal brain image segmentation is crucial for understanding development and disease.
• Existing methods face challenges due to limited data, varied acquisition protocols, and head positioning.
• Current automated pipelines often require time-consuming registration and resampling, leading to information loss.

Purpose of the Study:

• To develop a novel deep learning framework for robust and accurate neonatal brain MRI segmentation.
• To address limitations of traditional external augmentation techniques that require image resampling.
• To introduce resolution-aware internal augmentations within the neural network architecture.

Main Methods:

• Introduced Voxel-size Independent Neural Network (VINNA), incorporating a four degree of freedom (4-DOF) transform module.
• Enabled resolution-aware internal augmentations directly within the network architecture.
• Avoided image and label interpolation by performing spatial augmentations internally.

Main Results:

• VINNA significantly outperformed state-of-the-art external augmentation methods.
• The framework effectively handled head position variations common in newborn datasets.
• High segmentation accuracy was maintained across a range of resolutions (0.5-1.0 mm).

Conclusions:

• VINNA offers a powerful and generalizable approach for spatial augmentation without interpolation.
• The method significantly improves neonatal brain image segmentation accuracy and robustness.
• The VINNA4neonates application will be publicly released to advance research in this field.