Reproducible White Matter Tract Segmentation Using 3D U-Net on a Large-scale DTI Dataset

TL;DR

This paper introduces a deep learning approach using 3D U-Net for automatic, reproducible white matter tract segmentation in large-scale diffusion MRI datasets, aiming for clinical applicability.

Contribution

A novel CNN-based method for white matter tract segmentation trained on a large DTI dataset, optimized for accuracy and reproducibility, surpassing traditional tractography methods.

Findings

Higher reproducibility than reference standards

Consistent diffusion measure determination

Suitable for clinical and longitudinal studies

Abstract

Tract-specific diffusion measures, as derived from brain diffusion MRI, have been linked to white matter tract structural integrity and neurodegeneration. As a consequence, there is a large interest in the automatic segmentation of white matter tract in diffusion tensor MRI data. Methods based on the tractography are popular for white matter tract segmentation. However, because of the limited consistency and long processing time, such methods may not be suitable for clinical practice. We therefore developed a novel convolutional neural network based method to directly segment white matter tract trained on a low-resolution dataset of 9149 DTI images. The method is optimized on input, loss function and network architecture selections. We evaluated both segmentation accuracy and reproducibility, and reproducibility of determining tract-specific diffusion measures. The reproducibility of the method is higher than that of the reference standard and the determined diffusion measures are consistent. Therefore, we expect our method to be applicable in clinical practice and in longitudinal analysis of white matter microstructure.