Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis

TL;DR

This paper introduces a contrast adaptive method for brain MRI segmentation that alternates between segmentation and synthesis steps, enabling effective processing of images with different contrast properties from the training data.

Contribution

The method adaptively adjusts contrast properties using a single example, improving segmentation robustness across heterogeneous MRI datasets.

Findings

Effective segmentation across different MRI contrast protocols

Requires only one example of the new contrast for adaptation

Improves consistency in heterogeneous brain image datasets

Abstract

Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images. However, a continuing challenge has been its sensitivity to the variability of acquisition protocols. Attempting to segment images that have different contrast properties from those within the training data generally leads to significantly reduced performance. Furthermore, heterogeneous data sets cannot be easily evaluated because the quantitative variation due to acquisition differences often dwarfs the variation due to the biological differences that one seeks to measure. In this work, we describe an approach using alternating segmentation and synthesis steps that adapts the contrast properties of the training data to the input image. This allows input images that do not resemble the training data to be more consistently segmented. A notable advantage of this approach is that only a single example of the acquisition protocol is required to adapt to its contrast properties. We demonstrate the efficacy of our approaching using brain images from a set of human subjects scanned with two different T1-weighted volumetric protocols.