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Automated Identification of Injury Dynamics After Neonatal Hypoxia-Ischemia

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Video Bioinformatics

Part of the book series: Computational Biology ((COBO,volume 22))

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Abstract

Neonatal hypoxic ischemic injury (HII) is a devastating brain disease for which hypothermia is currently the only approved treatment. As new therapeutic interventions emerge there is a significant need for noninvasive objective quantification of the spatiotemporal lesion dynamics combined with precise information about lesion constituents (ischemic core and penumbra). These metrics are important for deciding treatment parameters (type, time, site, and dose) and for monitoring injury-therapy interactions. Such information provided ‘on-line’ in a timely fashion to the clinician could revolutionize clinical management. Like other spatiotemporal biological processes, video bioinformatics can assist objective monitoring of injury–therapy interactions. We have been studying the efficacy of various potential treatments in translational (rodent) HII models using magnetic resonance imaging (MRI). We have developed a novel computational tool, hierarchical region splitting (HRS) to rapidly identify ischemic lesion metrics. HRS detects similar lesion volumes compared to manual detection methods and is fast, robust, and reliable compared to other computational methods. HRS also provides additional information about the location, size, and evolution of the core and penumbra, which are difficult to ascertain with manual methods. This chapter summarizes the ability of HRS to identify lesion dynamics and ischemic core-penumbra evolution following neonatal HII. In addition, we demonstrate that HRS provides information about lesion dynamics following different therapies (e.g., hypothermia, stem cell implantation). Our findings show that computational analysis of MR images using HRS provides novel quantitative approaches that can be applied to clinical and translational stroke research using data mining of standard experimental and clinical MRI data.

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Abbreviations

ADC :

Apparent diffusion coefficient

CP :

Core-penumbra

CVPR :

Computer vision and pattern recognition

DPM :

Diffusion-perfusion mismatch

DWI :

Diffusion-weighted imaging

EM :

Expectation-maximization

HII :

Hypoxic ischemic injury

HRS :

Hierarchical region splitting

HT :

Hypothermia

IHC :

Immunohistochemistry

MR :

Magnetic resonance

MRI :

Magnetic resonance imaging

MWS :

Modified watershed

NIH :

National Institute of Health

NSC :

Neural stem cell

NT :

Normothermia

PWI :

Perfusion-weighted imaging

ROI :

Region of interest

RPSS :

Rat pup severity score

SIRG :

Symmetry-integrated region growing

SWI :

Susceptibility weighted imaging

T2WI :

T2-weighted imaging

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Acknowledgment

This research work has been supported by grants from the Department of Pediatrics (School of Medicine, Loma Linda University) and NIH-NINDS (1RO1NS059770-01A2). The authors would also like to acknowledge kind sharing of comparative results from SIRG and MWS methods by Dr. Yu Sun and Prof. Bir Bhanu in the Center of Research in Intelligent Systems (Department of Electrical and Computer Engineering, University of California at Riverside).

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Authors and Affiliations

  1. Department of Pediatrics School of Medicine, Loma Linda University, Loma Linda, CA, USA

    Nirmalya Ghosh, Stephen Ashwal & Andre Obenaus

Authors
  1. Nirmalya Ghosh
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  2. Stephen Ashwal
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  3. Andre Obenaus
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Corresponding author

Correspondence to Nirmalya Ghosh .

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Editors and Affiliations

  1. University of California, Riverside, California, USA

    Bir Bhanu

  2. University of California, Riverside, California, USA

    Prue Talbot

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Ghosh, N., Ashwal, S., Obenaus, A. (2015). Automated Identification of Injury Dynamics After Neonatal Hypoxia-Ischemia. In: Bhanu, B., Talbot, P. (eds) Video Bioinformatics. Computational Biology, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-23724-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-23724-4_4

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