Neonatal brain MRI segmentation: A review

Devi, Chelli N.; Chandrasekharan, Anupama; Sundararaman, Vijayaraghavan; Alex, Zachariah C.

doi:10.1016/j.compbiomed.2015.06.016

Cited by 87 publications

(55 citation statements)

References 102 publications

(206 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A variety of techniques have been proposed for tissue segmentation of the neonatal brain in recent years: unsupervised techniques (Gui et al, 2012), atlas fusion techniques (Weisenfeld and Warfield, 2009;Gousias et al, 2013;Kim et al, 2016), parametric techniques (Prastawa et al, 2005;Song et al, 2007;Xue et al, 2007;Shi et al, 2010;Cardoso et al, 2013;Makropoulos et al, 2012;Wang et al, 2012;Wu and Avants, 2012;Beare et al, 2016;Liu et al, 2016), classification techniques (Anbeek et al, 2008;Srhoj-Egekher et al, 2012;Chiţȃ et al, 2013;Wang et al, 2015;Sanroma et al, 2016;Moeskops et al, 2016) and deformable models (Wang et al, 2011;Dai et al, 2013;Wang et al, 2013Wang et al, , 2014. A review of neonatal segmentation methods can be found in Devi et al (2015); Makropoulos et al (2017). The majority of these techniques have been applied to images with a lower resolution than those acquired within the dHCP, and typically to images of preterm-born subjects.…”

Section: Introductionmentioning

confidence: 99%

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

et al. 2018

View full text Add to dashboard Cite

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline * Corresponding author Email address: a.makropoulos11@imperial.ac.uk (Antonios Makropoulos) 1 These authors contributed equally Preprint submitted to NeuroImage January 7, 2018peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/125526 doi: bioRxiv preprint first posted online Apr. 10, 2017; consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

show abstract

Section: Introductionmentioning

confidence: 99%

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

et al. 2018

View full text Add to dashboard Cite

show abstract

“…A review of neonatal brain MRI segmentation methods can be found in (Devi et al, 2015). To the best of our knowledge, only few studies focus on the segmentation of 6-month infant brain images with risk of autism.…”

Section: Introductionmentioning

confidence: 99%

“…Corresponding thickness maps shown on the outer surface by LINKS and the proposed work are provided in (e) and (h), respectively [Color figure can be viewed at wileyonlinelibrary.com] (<3 months of age) brain images with relatively high tissue contrast in T2-weighted image. Related reviews on neonatal brain MRI segmentation methods can be found in Devi, Chandrasekharan, Sundararaman, and Alex (2015) and Makropoulos, Counsell, and Rueckert (2017). To the best of our knowledge, only few studies focus on the segmentation of 6-month infant brain images with risk of autism.…”

mentioning

confidence: 99%

Anatomy‐guided joint tissue segmentation and topological correction for 6‐month infant brain MRI with risk of autism

Wang

Adeli

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

Tissue segmentation of infant brain MRIs with risk of autism is critically important for characterizing early brain development and identifying biomarkers. However, it is challenging due to low tissue contrast caused by inherent ongoing myelination and maturation. In particular, at around 6 months of age, the voxel intensities in both gray matter and white matter are within similar ranges, thus leading to the lowest image contrast in the first postnatal year. Previous studies typically employed intensity images and tentatively estimated tissue probabilities to train a sequence of classifiers for tissue segmentation. However, the important prior knowledge of brain anatomy is largely ignored during the segmentation. Consequently, the segmentation accuracy is still limited and topological errors frequently exist, which will significantly degrade the performance of subsequent analyses. Although topological errors could be partially handled by retrospective topological correction methods, their results may still be anatomically incorrect. To address these challenges, in this article, we propose an anatomy-guided joint tissue segmentation and topological correction framework for isointense infant MRI. Particularly, we adopt a signed distance map with respect to the outer cortical surface as anatomical prior knowledge, and incorporate such prior information into the proposed framework to guide segmentation in ambiguous regions. Experimental results on the subjects acquired from National Database for Autism Research demonstrate the effectiveness to topological errors and also some levels of robustness to motion. Comparisons with the state-of-the-art methods further demonstrate the advantages of the proposed method in terms of both segmentation accuracy and topological correctness.

show abstract

“…Atlas-based segmentation is a well-established concept that has been widely applied to brain structures [166–168]. Its application to adipose tissue segmentation is a logical extension, and the paradigm is schematically illustrated in Fig.…”

Section: Common Segmentation Stepsmentioning

confidence: 99%

Segmentation and quantification of adipose tissue by magnetic resonance imaging

Chen

Shen

2015

Magn Reson Mater Phy

View full text Add to dashboard Cite

In this brief review, introductory concepts in animal and human adipose tissue segmentation using proton magnetic resonance imaging (MRI) and computed tomography are summarized in the context of obesity research. Adipose tissue segmentation and quantification using spin relaxation-based (e.g., T1-weighted, T2-weighted), relaxometry-based (e.g., T1-, T2-, T2*-mapping), chemical-shift selective, and chemical-shift encoded water–fat MRI pulse sequences are briefly discussed. The continuing interest to classify subcutaneous and visceral adipose tissue depots into smaller sub-depot compartments is mentioned. The use of a single slice, a stack of slices across a limited anatomical region, or a whole body protocol is considered. Common image post-processing steps and emerging atlas-based automated segmentation techniques are noted. Finally, the article identifies some directions of future research, including a discussion on the growing topic of brown adipose tissue and related segmentation considerations.

show abstract

Neonatal brain MRI segmentation: A review

Cited by 87 publications

References 102 publications

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

Anatomy‐guided joint tissue segmentation and topological correction for 6‐month infant brain MRI with risk of autism

Segmentation and quantification of adipose tissue by magnetic resonance imaging

Contact Info

Product

Resources

About