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A fast segmentation-free fully automated approach to white matter injury detection in preterm infants

Medical & Biological Engineering & Computing volume 57, pages 71–87 (2019)Cite this article

Abstract

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.

Key Steps of Segmentation-free WMI Detection

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Funding

Financial support from CIHR, NeuroDevNet, Alberta Innovates (iCORE) Research Chair program, and NSERC in conducting this research is gratefully acknowledged.

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Affiliations

  1. Department of Computing Science, University of Alberta, 402 Athabasca Hall, Edmonton, Alberta, T6G 2H1, Canada

    Subhayan Mukherjee, Irene Cheng & Anup Basu

  2. The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, Canada

    Steven Miller, Ting Guo & Vann Chau

Authors
  1. Subhayan Mukherjee
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  2. Irene Cheng
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  3. Steven Miller
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  4. Ting Guo
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  5. Vann Chau
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  6. Anup Basu
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Corresponding author

Correspondence to Anup Basu.

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Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The images provided were from a research project for which parents provided consent.

We followed the “Standard Protocol Approvals, Registration, and Patient Consents” at the BC Children’s Hospital in Vancouver. A written informed consent from the legal guardian of each participating neonate was obtained. This study was reviewed and approved by the Clinical Research Ethics Board at the University of British Columbia and BC Children’s and Women’s Hospitals.

Conflict of interests

The authors declare that they have no conflict of interest.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Cite this article

Mukherjee, S., Cheng, I., Miller, S. et al. A fast segmentation-free fully automated approach to white matter injury detection in preterm infants. Med Biol Eng Comput 57, 71–87 (2019). https://doi.org/10.1007/s11517-018-1829-9

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  • DOI: https://doi.org/10.1007/s11517-018-1829-9

Keywords

  • White matter injury
  • Segmentation
  • Magnetic resonance imaging
  • Preterm newborn
  • Atlas-free