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

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