Learning-based segmentation of diffusion-weighted MR images with arbitrary q-space samplings.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Computational Neuroscience

Background:

• Anatomical segmentation is vital for diffusion-weighted MRI (dMRI) analysis, enabling in vivo studies of brain microstructure and connectivity.
• Convolutional Neural Networks (CNNs), dominant in segmentation, struggle with dMRI's unstructured, variable data due to inconsistent q-space sampling, limiting their generalizability.

Purpose of the Study:

• To develop a novel method for direct anatomical segmentation of dMRI data, overcoming the limitations of CNNs with structured inputs and improving generalization across diverse acquisition schemes.

Main Methods:

• Combined the DISCUS geometric deep learning framework with the VINN segmentation network to directly process unstructured dMRI data.
• Developed a novel segmentation approach that achieves robust generalization across heterogeneous acquisition schemes using a single neural network.

Main Results:

• The proposed method achieves robust generalization across heterogeneous dMRI acquisition schemes without requiring diffusion model fits.
• Segmentation is generated in minutes, significantly faster than methods like DeepAnat (hours).
• Demonstrated superior segmentation performance compared to DeepAnat, DDParcel, and SynthSeg across multiple datasets and acquisition settings.

Conclusions:

• This work presents the first deep learning segmentation approach for dMRI that directly maps unstructured data to anatomical segmentations, achieving robust generalization.
• The method offers a faster and more accurate alternative for dMRI anatomical segmentation, enhancing the applicability of dMRI analysis in neuroscience research.