A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants

TL;DR

This paper introduces a rapid, segmentation-free, unsupervised method for detecting white matter injury in preterm infants' brain MRIs, overcoming the limitations of traditional segmentation-based approaches.

Contribution

It presents a novel, fast, and atlas-free WMI detection technique that significantly improves speed without sacrificing accuracy compared to previous segmentation-based methods.

Findings

Method runs 20 times faster than previous approaches.

Detection accuracy is maintained despite increased speed.

Effective identification of WMI in preterm neonate brains.

Abstract

White Matter Injury (WMI) is the most prevalent brain injury in the preterm neonate leading to developmental deficits. However, detecting WMI in Magnetic Resonance (MR) images of preterm neonate brains using traditional WM segmentation-based methods is difficult mainly due to lack of reliable preterm neonate brain atlases to guide segmentation. Hence, we propose a segmentation-free, fast, unsupervised, atlas-free WMI detection method. We detect the ventricles as blobs using a fast linear Maximally Stable Extremal Regions algorithm. A reference contour equidistant from the blobs and the brain-background boundary is used to identify tissue adjacent to the blobs. Assuming normal distribution of the gray-value intensity of this tissue, the outlier intensities in the entire brain region are identified as potential WMI candidates. Thereafter, false positives are discriminated using appropriate heuristics. Experiments using an expert-annotated dataset show that the proposed method runs 20 times faster than our earlier work which relied on time-consuming segmentation of the WM region, without compromising WMI detection accuracy.