A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants
This addresses the challenge of WMI detection for preterm infants, offering a faster and atlas-free solution, though it appears incremental as it builds on earlier work.
The paper tackles the problem of detecting White Matter Injury (WMI) in preterm infant brain MR images by proposing a segmentation-free, unsupervised method that runs 20 times faster than previous approaches without compromising accuracy.
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.