Optimal operating MR contrast for brain ventricle parcellation

TL;DR

This paper investigates how different T1-weighted MRI contrasts affect ventricle parcellation performance and identifies an optimal contrast to enhance the accuracy of a state-of-the-art algorithm.

Contribution

It introduces the concept of an optimal operating contrast (OOC) for ventricle parcellation and demonstrates performance improvements by adjusting contrast to the OOC.

Findings

Optimal contrast improves parcellation accuracy

Pretrained model performance is boosted by contrast adjustment

Contrast harmonization benefits ventricle segmentation

Abstract

Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy. In this paper, we use image harmonization to explore the impact of different T1-w MR contrasts on a state-of-the-art ventricle parcellation algorithm VParNet. We identify an optimal operating contrast (OOC) for ventricle parcellation; by showing that the performance of a pretrained VParNet can be boosted by adjusting contrast to the OOC.