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

arXiv: https://arxiv.org/abs/1807.06604 · HTML: https://mlsanity.com/paper/1807.06604/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [Punctate White Matter Lesion Segmentation in Preterm Infants Powered by Counterfactually Generative Learning](https://mlsanity.com/paper/2309.03440/) (2023)

## Abstract (arXiv)

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.