A Multi‐Atlas Label Fusion Tool for Neonatal Brain MRI Parcellation and Quantification

Structure‐by‐structure analysis, in which the brain magnetic resonance imaging (MRI) is parcellated based on its anatomical units, is widely used to investigate chronological changes in morphology or signal intensity during normal development, as well as to identify the alterations seen in various diseases or conditions. The multi‐atlas label fusion (MALF) method is considered a highly accurate parcellation approach, and anticipated for clinical application to quantitatively evaluate early developmental processes. However, the current MALF methods, which are designed for neonatal brain segmentations, are not widely available. In this study, we developed a T1‐weighted, neonatal, multi‐atlas repository and integrated it into the MALF‐based brain segmentation tools in the cloud‐based platform, MRICloud. The cloud platform ensures users instant access to the advanced MALF tool for neonatal brains, with no software or installation requirements for the client. The Web platform by braingps.mricloud.org will eliminate the dependence on a particular operating system (eg, Windows, Macintosh, or Linux) and the requirement for high computational performance of the user's computers. The MALF‐based, fully automated, image parcellation could achieve excellent agreement with manual parcellation, and the whole and regional brain volumes quantified through this method demonstrated developmental trajectories comparable to those from a previous publication. This solution will make the latest MALF tools readily available to users, with minimum barriers, and will expedite and accelerate advancements in developmental neuroscience research, neonatology, and pediatric neuroradiology.

[1]  Daniel Rueckert,et al.  Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation , 2010, NeuroImage.

[2]  Linda Chang,et al.  Delayed early developmental trajectories of white matter tracts of functional pathways in preterm-born infants: Longitudinal diffusion tensor imaging data , 2016, Data in brief.

[3]  Dan Wu,et al.  Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI , 2017, NeuroImage.

[4]  Xiaoying Tang,et al.  Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool , 2015, PloS one.

[5]  Indika S. Walimuni,et al.  Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls: Implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS , 2012, Journal of the Neurological Sciences.

[6]  Benoit M. Dawant,et al.  Morphometric analysis of white matter lesions in MR images: method and validation , 1994, IEEE Trans. Medical Imaging.

[7]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[8]  Paul A. Yushkevich,et al.  Multi-Atlas Segmentation with Joint Label Fusion , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Chris Adamson,et al.  A new neonatal cortical and subcortical brain atlas: the Melbourne Children's Regional Infant Brain (M-CRIB) atlas , 2017, NeuroImage.

[10]  J. Bodensteiner,et al.  Does selection bias determine the prevalence of the cavum septi pellucidi? , 1998, Pediatric neurology.

[11]  S. Mori,et al.  Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging , 2013, International Journal of Developmental Neuroscience.

[12]  Linda Chang,et al.  Construction and application of human neonatal DTI atlases , 2015, Front. Neuroanat..

[13]  Dinggang Shen,et al.  Multi-atlas Based Simultaneous Labeling of Longitudinal Dynamic Cortical Surfaces in Infants , 2013, MICCAI.

[14]  Chi‐Jen Chen,et al.  Prevalence of cavum septum pellucidum and/or cavum Vergae in brain computed tomographies of Taiwanese. , 2014, Acta neurologica Taiwanica.

[15]  Paul M. Thompson,et al.  Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods , 2011, IEEE Transactions on Medical Imaging.

[16]  J. Bodensteiner,et al.  The Cavum Septi Pellucidi in Term and Preterm Newborn Infants , 1992, Journal of child neurology.

[17]  U. Grenander,et al.  Computational anatomy: an emerging discipline , 1998 .

[18]  Dinggang Shen,et al.  Multi-atlas and Multi-modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph , 2015, Patch-MI@MICCAI.

[19]  Carlos Ortiz-de-Solorzano,et al.  Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data , 2009, IEEE Transactions on Medical Imaging.

[20]  H. Hakonarson,et al.  Incidental Findings in Youths Volunteering for Brain MRI Research , 2013, American Journal of Neuroradiology.

[21]  Guido Gerig,et al.  Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆ , 2012, NeuroImage: Clinical.

[22]  Mark E. Bastin,et al.  Parcellation of the Healthy Neonatal Brain into 107 Regions Using Atlas Propagation through Intermediate Time Points in Childhood , 2016, Front. Neurosci..

[23]  Dinggang Shen,et al.  The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development , 2019, NeuroImage.

[24]  A. Dale,et al.  Structural growth trajectories and rates of change in the first 3 months of infant brain development. , 2014, JAMA neurology.

[25]  Daniel Rueckert,et al.  The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction , 2017, NeuroImage.

[26]  Y. Otsuka,et al.  Neonatal brain MRI multi-atlas repository for automated image quantification , 2017, Journal of the Neurological Sciences.

[27]  Michael W. L. Chee,et al.  Skull stripping using graph cuts , 2010, NeuroImage.

[28]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[29]  D. Louis Collins,et al.  Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation , 2011, NeuroImage.

[30]  Daniel Rueckert,et al.  Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy , 2009, NeuroImage.

[31]  Michael I. Miller,et al.  Multi-contrast human neonatal brain atlas: Application to normal neonate development analysis , 2011, NeuroImage.

[32]  Daoqiang Zhang,et al.  Robust multi-atlas label propagation by deep sparse representation , 2017, Pattern Recognit..

[33]  Paul A. Yushkevich,et al.  Multi-atlas Segmentation with Robust Label Transfer and Label Fusion , 2013, IPMI.

[34]  Xiaoying Tang,et al.  Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain , 2014, PloS one.

[35]  Oualid M. Benkarim,et al.  Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation , 2018, Comput. Medical Imaging Graph..

[36]  M. Mallar Chakravarty,et al.  Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates , 2014, NeuroImage.

[37]  R. Leahy,et al.  Magnetic Resonance Image Tissue Classification Using a Partial Volume Model , 2001, NeuroImage.

[38]  Michael I. Miller,et al.  Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI , 2016, NeuroImage.

[39]  Michael I. Miller,et al.  Volumetric transformation of brain anatomy , 1997, IEEE Transactions on Medical Imaging.

[40]  U. Grenander,et al.  Statistical methods in computational anatomy , 1997, Statistical methods in medical research.

[41]  Thomas Ernst,et al.  Probabilistic maps of the white matter tracts with known associated functions on the neonatal brain atlas: Application to evaluate longitudinal developmental trajectories in term-born and preterm-born infants , 2016, NeuroImage.

[42]  Dwarikanath Mahapatra,et al.  Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts , 2012, Journal of Digital Imaging.

[43]  Henry Rusinek,et al.  Fully automatic segmentation of the brain from T1‐weighted MRI using Bridge Burner algorithm , 2008, Journal of magnetic resonance imaging : JMRI.

[44]  Bennett A. Landman,et al.  Non-local STAPLE: An Intensity-Driven Multi-atlas Rater Model , 2012, MICCAI.

[45]  Joanna M. Wardlaw,et al.  Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging , 2017, Front. Neuroinform..

[46]  William M. Wells,et al.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation , 2004, IEEE Transactions on Medical Imaging.

[47]  Hao Huang,et al.  Baby brain atlases , 2019, NeuroImage.

[48]  Bilwaj Gaonkar,et al.  Multi-atlas skull-stripping. , 2013, Academic radiology.

[49]  Daniel Rueckert,et al.  Magnetic resonance imaging of the newborn brain: Manual segmentation of labelled atlases in term-born and preterm infants , 2012, NeuroImage.

[50]  E. Teasdale,et al.  CT demonstration of a 5th ventricle—a finding to KO boxers? , 2004, Neuroradiology.

[51]  Michael I. Miller,et al.  A Bayesian approach to the creation of a study-customized neonatal brain atlas , 2014, NeuroImage.

[52]  Christos Davatzikos,et al.  Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases , 2018, NeuroImage.

[53]  Daniel Rueckert,et al.  Magnetic Resonance Imaging of the Newborn Brain: Automatic Segmentation of Brain Images into 50 Anatomical Regions , 2013, PloS one.