A fast segmentation-free fully automated approach to white matter injury detection in preterm infants.
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 preter...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 1; pp. 71 - 88 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133800676&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133800676 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2019 vid: 57 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133800676 133800676 NLM29981051 133800676 10.1007/s11517-018-1829-9 NLM29981051 133800676 ppf: 71 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A fast segmentation-free fully automated approach to white matter injury detection in preterm infants. aug: au: Mukherjee, Subhayan Cheng, Irene Miller, Steven Guo, Ting Chau, Vann Basu, Anup affil: Department of Computing Science, University of Alberta, 402 Athabasca Hall, T6G 2H1, Edmonton, Alberta, Canada sug: subj: Image Processing, Computer Assisted Brain Infant, Premature Brain Pathology Magnetic Resonance Imaging False Positive Results Infant, Newborn Time Factors Infant, Newborn: birth-1 month ab: 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. Graphical Abstract Key Steps of Segmentation-free WMI Detection. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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