TY - JOUR
T1 - A Multi-Atlas Label Fusion Tool for Neonatal Brain MRI Parcellation and Quantification
AU - Otsuka, Yoshihisa
AU - Chang, Linda
AU - Kawasaki, Yukako
AU - Wu, Dan
AU - Ceritoglu, Can
AU - Oishi, Kumiko
AU - Ernst, Thomas
AU - Miller, Michael
AU - Mori, Susumu
AU - Oishi, Kenichi
N1 - Funding Information:
Acknowledgments and disclosure: Parts of the preliminary results of this study were presented at the XXIII World Congress of Neurology. This work was made possible by grants R01HD065955, 2K24DA16170, and U54NS056883 from the National Institutes of Health (NIH). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official view of the NIH. The authors are grateful to the families of our research participants, the pediatricians/neonatologists who referred the participants (Dr. Lillian Fujimoto, Dr. Lois Chiu, and Dr. Joseph Hudak), and our dedicated research staff (Steven Buchthal, Eric Cunningham, Daniel Alicata, Heather Johansen, Antonette Hernandez, Robyn Yamakawa, Sara Hayama, Tamara Andres), who assisted with the data collection. We also thank our board-certified neuroradiologist, Dr. Doris Lin, for her radiological reading of the scans, Dr. Kunihiro Matsushita for his statistical advice, and Ms. Mary McAllister for help with manuscript editing. Dr. Miller and Dr. Mori are the cofounders, and Dr. Oishi is a consultant, for Anatomy Works. The terms of this arrangement are being managed by the Johns Hopkins University in accordance with its conflict of interest policies.
Funding Information:
and disclosure: Parts of the preliminary results of this study were presented at the XXIII World Congress of Neurology. This work was made possible by grants R01HD065955, 2K24DA16170, and U54NS056883 from the National Institutes of Health (NIH). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official view of the NIH. The authors are grateful to the families of our research participants, the pediatricians/neonatologists who referred the participants (Dr. Lillian Fujimoto, Dr. Lois Chiu, and Dr. Joseph Hudak), and our dedicated research staff (Steven Buchthal, Eric Cunningham, Daniel Alicata, Heather Johansen, Antonette Hernandez, Robyn Yamakawa, Sara Hayama, Tamara Andres), who assisted with the data collection. We also thank our board-certified neuroradiologist, Dr. Doris Lin, for her radiological reading of the scans, Dr. Kunihiro Matsushita for his statistical advice, and Ms. Mary McAllister for help with manuscript editing. Dr. Miller and Dr. Mori are the cofounders, and Dr. Oishi is a consultant, for Anatomy Works. The terms of this arrangement are being managed by the Johns Hopkins University in accordance with its conflict of interest policies.
Publisher Copyright:
© 2019 by the American Society of Neuroimaging
PY - 2019/7/1
Y1 - 2019/7/1
N2 - 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.
AB - 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.
KW - Brain
KW - MRI
KW - multi-atlas
KW - neonate
KW - parcellation
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U2 - 10.1111/jon.12623
DO - 10.1111/jon.12623
M3 - Article
C2 - 31037800
AN - SCOPUS:85065215536
SN - 1051-2284
VL - 29
SP - 431
EP - 439
JO - Journal of Neuroimaging
JF - Journal of Neuroimaging
IS - 4
ER -