title: Automated detection of brain abnormalities in neonatal hypoxia ischemic injury from MR images.
abstract: We compared the efficacy of three automated brain injury detection methods, namely symmetry-integrated region growing (SIRG), hierarchical region splitting (HRS) and modified watershed segmentation (MWS) in human and animal magnetic resonance imaging (MRI) datasets for the detection of hypoxic ischemic injuries (HIIs). Diffusion weighted imaging (DWI, 1.5T) data from neonatal arterial ischemic stroke (AIS) patients, as well as T2-weighted imaging (T2WI, 11.7T, 4.7T) at seven different time-points (1, 4, 7, 10, 17, 24 and 31 days post HII) in rat-pup model of hypoxic ischemic injury were used to assess the temporal efficacy of our computational approaches. Sensitivity, specificity, and similarity were used as performance metrics based on manual ('gold standard') injury detection to quantify comparisons. When compared to the manual gold standard, automated injury location results from SIRG performed the best in 62% of the data, while 29% for HRS and 9% for MWS. Injury severity detection revealed that SIRG performed the best in 67% cases while 33% for HRS. Prior information is required by HRS and MWS, but not by SIRG. However, SIRG is sensitive to parameter-tuning, while HRS and MWS are not. Among these methods, SIRG performs the best in detecting lesion volumes; HRS is the most robust, while MWS lags behind in both respects.
authors.affiliation: Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA.
authors.forename: Nirmalya
authors.initials: N
authors.lastname: Ghosh
authors.affiliation: Center for Research in Intelligent Systems (CRIS), University of California, Riverside, CA 92521, USA.
authors.forename: Yu
authors.initials: Y
authors.lastname: Sun
authors.affiliation: Center for Research in Intelligent Systems (CRIS), University of California, Riverside, CA 92521, USA.
authors.forename: Bir
authors.initials: B
authors.lastname: Bhanu
authors.affiliation: Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA.
authors.forename: Stephen
authors.initials: S
authors.lastname: Ashwal
authors.affiliation: Department of Pediatrics, Loma Linda University, School of Medicine, Loma Linda, CA 92354, USA; Cell, Molecular and Developmental Biology Program and Department of Neuroscience, University of California, 1140 Bachelor Hall, Riverside, CA 92521, USA. Electronic address: aobenaus@llu.edu.
authors.forename: Andre
authors.initials: A
authors.lastname: Obenaus
mesh_terms: D000465:Algorithms; D000818:Animals; D000831:Animals, Newborn; D006801:Humans; D020925:Hypoxia-Ischemia, Brain / Q000473:pathology*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D007232:Infant, Newborn, Diseases / Q000473:pathology*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D051381:Rats; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Arterial ischemic stroke; Hierarchical region splitting; Hypoxia ischemic injury; Symmetry; Watershed