期刊
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
卷 57, 期 1, 页码 71-87出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s11517-018-1829-9
关键词
White matter injury; Segmentation; Magnetic resonance imaging; Preterm newborn; Atlas-free
类别
资金
- CIHR
- NeuroDevNet
- Alberta Innovates (iCORE) Research Chair program
- NSERC
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据