Morphology-driven automatic segmentation of MR images of the neonatal brain - PubMed (original) (raw)
. 2012 Dec;16(8):1565-79.
doi: 10.1016/j.media.2012.07.006. Epub 2012 Jul 31.
Affiliations
- PMID: 22921305
- DOI: 10.1016/j.media.2012.07.006
Morphology-driven automatic segmentation of MR images of the neonatal brain
Laura Gui et al. Med Image Anal. 2012 Dec.
Abstract
The segmentation of MR images of the neonatal brain is an essential step in the study and evaluation of infant brain development. State-of-the-art methods for adult brain MRI segmentation are not applicable to the neonatal brain, due to large differences in structure and tissue properties between newborn and adult brains. Existing newborn brain MRI segmentation methods either rely on manual interaction or require the use of atlases or templates, which unavoidably introduces a bias of the results towards the population that was used to derive the atlases. We propose a different approach for the segmentation of neonatal brain MRI, based on the infusion of high-level brain morphology knowledge, regarding relative tissue location, connectivity and structure. Our method does not require manual interaction, or the use of an atlas, and the generality of its priors makes it applicable to different neonatal populations, while avoiding atlas-related bias. The proposed algorithm segments the brain both globally (intracranial cavity, cerebellum, brainstem and the two hemispheres) and at tissue level (cortical and subcortical gray matter, myelinated and unmyelinated white matter, and cerebrospinal fluid). We validate our algorithm through visual inspection by medical experts, as well as by quantitative comparisons that demonstrate good agreement with expert manual segmentations. The algorithm's robustness is verified by testing on variable quality images acquired on different machines, and on subjects with variable anatomy (enlarged ventricles, preterm- vs. term-born).
Copyright © 2012 Elsevier B.V. All rights reserved.
Similar articles
- Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge.
Išgum I, Benders MJ, Avants B, Cardoso MJ, Counsell SJ, Gomez EF, Gui L, Hűppi PS, Kersbergen KJ, Makropoulos A, Melbourne A, Moeskops P, Mol CP, Kuklisova-Murgasova M, Rueckert D, Schnabel JA, Srhoj-Egekher V, Wu J, Wang S, de Vries LS, Viergever MA. Išgum I, et al. Med Image Anal. 2015 Feb;20(1):135-51. doi: 10.1016/j.media.2014.11.001. Epub 2014 Nov 15. Med Image Anal. 2015. PMID: 25487610 - An automated registration algorithm for measuring MRI subcortical brain structures.
Iosifescu DV, Shenton ME, Warfield SK, Kikinis R, Dengler J, Jolesz FA, McCarley RW. Iosifescu DV, et al. Neuroimage. 1997 Jul;6(1):13-25. doi: 10.1006/nimg.1997.0274. Neuroimage. 1997. PMID: 9245652 Clinical Trial. - Derivation of high-resolution MRI atlases of the human cerebellum at 3T and segmentation using multiple automatically generated templates.
Park MT, Pipitone J, Baer LH, Winterburn JL, Shah Y, Chavez S, Schira MM, Lobaugh NJ, Lerch JP, Voineskos AN, Chakravarty MM. Park MT, et al. Neuroimage. 2014 Jul 15;95:217-31. doi: 10.1016/j.neuroimage.2014.03.037. Epub 2014 Mar 21. Neuroimage. 2014. PMID: 24657354 - Neonatal brain MRI segmentation: A review.
Devi CN, Chandrasekharan A, Sundararaman VK, Alex ZC. Devi CN, et al. Comput Biol Med. 2015 Sep;64:163-78. doi: 10.1016/j.compbiomed.2015.06.016. Epub 2015 Jun 29. Comput Biol Med. 2015. PMID: 26189155 Review. - A review of atlas-based segmentation for magnetic resonance brain images.
Cabezas M, Oliver A, Lladó X, Freixenet J, Cuadra MB. Cabezas M, et al. Comput Methods Programs Biomed. 2011 Dec;104(3):e158-77. doi: 10.1016/j.cmpb.2011.07.015. Epub 2011 Aug 25. Comput Methods Programs Biomed. 2011. PMID: 21871688 Review.
Cited by
- Subject-specific atlas for automatic brain tissue segmentation of neonatal magnetic resonance images.
Noorizadeh N, Kazemi K, Taji SM, Danyali H, Aarabi A. Noorizadeh N, et al. Sci Rep. 2024 Aug 18;14(1):19114. doi: 10.1038/s41598-024-69995-z. Sci Rep. 2024. PMID: 39155321 Free PMC article. - Amygdala volumes and associations with socio-emotional competencies in preterm youth: cross-sectional and longitudinal data.
Pereira Camejo M, Escobar Saade L, Liverani MC, Fischi-Gomez E, Gui L, Borradori Tolsa C, Ha-Vinh Leuchter R, Hüppi PS, Siffredi V. Pereira Camejo M, et al. Pediatr Res. 2024 May 18. doi: 10.1038/s41390-024-03227-y. Online ahead of print. Pediatr Res. 2024. PMID: 38762662 - Deep learning techniques for isointense infant brain tissue segmentation: a systematic literature review.
Mhlanga ST, Viriri S. Mhlanga ST, et al. Front Med (Lausanne). 2023 Dec 18;10:1240360. doi: 10.3389/fmed.2023.1240360. eCollection 2023. Front Med (Lausanne). 2023. PMID: 38193036 Free PMC article. - A Novel Nomogram Based on Quantitative MRI and Clinical Features for the Prediction of Neonatal Intracranial Hypertension.
Qin Y, Liu Y, Cao C, Ouyang L, Ding Y, Wang D, Zheng M, Liao Z, Yue S, Liao W. Qin Y, et al. Children (Basel). 2023 Sep 22;10(10):1582. doi: 10.3390/children10101582. Children (Basel). 2023. PMID: 37892245 Free PMC article. - Development of cortical folds in the human brain: An attempt to review biological hypotheses, early neuroimaging investigations and functional correlates.
de Vareilles H, Rivière D, Mangin JF, Dubois J. de Vareilles H, et al. Dev Cogn Neurosci. 2023 Jun;61:101249. doi: 10.1016/j.dcn.2023.101249. Epub 2023 Apr 25. Dev Cogn Neurosci. 2023. PMID: 37141790 Free PMC article. Review.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Medical