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 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.
KeywordsWhite matter injury Segmentation Magnetic resonance imaging Preterm newborn Atlas-free
Financial support from CIHR, NeuroDevNet, Alberta Innovates (iCORE) Research Chair program, and NSERC in conducting this research is gratefully acknowledged.
Compliance with Ethical Standards
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 was obtained from all individual participants included in the study.
- 1.Spm12 - statistical parametric mapping. http://www.fil.ion.ucl.ac.uk/spm/software/spm12/. Accessed: 2016-06-09
- 11.Donoser M (2006) 3d segmentation by maximally stable volumes (msvs). In: 18th International Conference on Pattern Recognition (ICPR’06), vol 1, pp 63–66Google Scholar
- 13.Farzan A (2014) Heuristically improved Bayesian segmentation of brain MR images. Sci World J 9(3):5–8Google Scholar
- 14.Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., BostonGoogle Scholar
- 18.Haralick RM, Shapiro LG (1992) Computer and robot vision, vol I. Addison-Wesley, BostonGoogle Scholar
- 19.Iglewicz B, Hoaglin DC (1993) How to detect and handle outliers. ASQC basic references in quality control. ASQC Quality PressGoogle Scholar
- 25.Li H, Yezzi A, Cohen LD (2005) Computer vision for biomedical image applications: first international workshop, CVBIA 2005, Beijing, China, October 21, 2005. Proceedings, chapter Fast 3D Brain Segmentation Using Dual-Front Active Contours with Optional User-Interaction. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 335–345Google Scholar
- 27.Matas J, Chum O, Urban M, Pajdla T (2002) Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the BMVC, pp 36.1–36.10. https://doi.org/10.5244/C.16.36
- 30.Miller SP, Ferriero DM, Leonard C, Piecuch R, Glidden DV, Partridge JC, Perez M, Mukherjee P, Vigneron DB, Barkovich AJ (2005) Early brain injury in premature newborns detected with magnetic resonance imaging is associated with adverse early neurodevelopmental outcome. J Pediatr 147(5): 609–616PubMedCrossRefGoogle Scholar
- 32.Nistér D, Stewénius H (2008) Linear time maximally stable extremal regions. In: Forsyth D, Torr P, Zisserman A (eds) Computer Vision – ECCV 2008, volume 5303 of Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 183–196Google Scholar
- 41.Dawood Salman S, Habash QA, Ahmed ZT (2012) 3d brain segmentation using active contour with multi labeling method. In: 2012 First National Conference for Engineering Sciences (FNCES), pp 1–4Google Scholar
- 42.San GLY, Lee ML, Hsu W (2012) Constrained-mser detection of retinal pathology. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp 2059–2062Google Scholar
- 46.Soille P (2004) Morphological image analysis. Springer Science + Business MediaGoogle Scholar