Journals [1-93] (including Neuron, Nature Methods, Nature Biomedical Engineering, Nature Human Behaviour, Nature Neuroscience, Nature Communications, PNAS, Neuroimage, Cell Reports, IEEE TMI, etc.)
Conferences/Abstracts [94-130] (including ICCV, MICCAI, ISMRM, OHBM, ISBI, EMBC, BioMag, etc.)
Theses/Dissertations [131-132]
[1] “The structural MRIs were preprocessed via cortical surface-based processing pipelines, including skull stripping, tissue segmentation, cortical reconstruction, and cortical surface measurement calculation. Code availability: iBEAT pipeline v1.0.0 (https://github.com/iBEAT-V2/iBEAT-V2.0-Docker)”
[2] “All the scans were processed with the infant brain extraction and analysis toolbox (iBEAT V2.0) (http://www.ibeat.cloud/).”
[3] “Qualitychecked BCP images were then minimally preprocessed using iBEAT V2.0, a state-of-the-art pipeline specifically optimized for infant and toddlerMRI data.”
[4] “Neonatal 3D-SPACE anatomical images underwent hippocampal extraction and tissue segmentation using the Infant Brain Extraction and Analysis Toolbox (iBEAT V2.0, http:// www. ibeat.cloud).”
[5] “An initial segmentation of gray and white matter was generated from the T1-weighted brain volume using infant FreeSurfer’s automatic segmentation code (infant-recon-all; version freesurfer-linux-centos7_x86_64-infant-dev- 4a14499-20210109; https://surfer.nmr.mgh.harvard.edu/fswiki/infantFS45). (2) A second segmentation was done using both the T1- and T2-weighted anatomical images and the brain extraction toolbox (Brain Extraction and Analysis Toolbox, iBEAT, v-2.0 cloud processing, https://ibeat.wildapricot.org/). iBEAT V2.0 was specifically designed for processing infant brain MRI data (0-2 years), using contrasts properties of both T1w and T2w images, and employs deep learning techniques trained on infant data to handle the unique challenges of low contrast between gray and white matter in developing brains. Through visual inspection, we determined that iBEAT provided a more accurate segmentation of the gray-white matter boundaries in our low-contrast infant images compared to the infant FreeSurfer approach.”
[6] “To improve tissue classification accuracy, MPRAGE images were submitted in parallel to iBEATv2.0 Docker 1.1.0 [https://github.com/iBEAT-V2/iBEAT-V2.0-Docker], which has been validated for birth to age 6 y.”
[7] “Source images were processed using the Infant Brain Extraction and Analysis Toolbox (iBEAT v2.0 Cloud; http://www.ibeat.cloud), which includes pipelines specifically designed for infant MRI and fully described in the original report.”
[8] “For participants aged 0–24 months, we used the iBEAT V2.0 pipelines. Compared with alternative approaches, this pipeline is optimized for preprocessing early-age neuroimaging data with advanced algorithms and shows superior performance in tissue segmentation and cortical reconstruction.”
[9] “Furthermore, we used the officially recommended iBEAT V2.0 pipelines for participants aged from 0–2 years (all from the Baby Connectome Project (BCP)).”
[10] “Tissue volumes (WM, GM, CSF, ventricle and hippocampus) and mean cortical thickness were extracted by iBEAT V2.0.”
[11] “All the scans were processed with the infant brain extraction and analysis toolbox (iBEAT V2.0) (http://www.ibeat.cloud/).”
[12] “Structural MR images were processed by iBEAT V2.0 pipeline (http://www.ibeat.cloud).”
[13] “Then, we used iBEAT V2.0 to segment the white and gray matter.”
[14] “Sixty-three preterm infants (42 with severe BPD and 21 without severe BPD) who underwent magnetic resonance imaging at term equivalent age (TEA) and 18 months of CA were studied by using the Infant Brain Extraction and Analysis Toolbox (iBEAT).”
[15] “Neonatal T1-weighted images underwent tissue segmentation using the Infant Brain Extraction and Analysis Toolbox (iBEAT) V2.0.”
[16] “The T1 and T2 scans were processed using Infant Brain Extraction and Analysis Toolbox (iBEAT V2.0 Cloud) for initial processing and brain segmentation.”
[17] “The next best performing model (iBEATv2) yielded an average Dice of 0.949 0.017.”
[18] “It leverages deep learning techniques and includes preprocessing, segmentation, and surface reconstruction. The pipeline effectively handles diverse imaging protocols and scanners. The study provides a website, iBEAT Cloud, for users to process their images.”
[19] “For processing the BCP dataset, we used an infant dedicated computational pipeline iBEAT V2.0 (http://www.ibeat.cloud/).”
[20] “Skull stripping and cerebellum extraction were performed using an infant cerebrum-dedicated pipeline (iBEAT V2.0, http://www.ibeat.cloud). ”
[21] “All T1-weighted and T2-weighted MR images were processed using an infant-dedicated computational pipeline (http://www.ibeat.cloud/) described previously.”
[22] “All images were then preprocessed using the infant brain extraction and analysis toolbox (iBEAT V2.0 Cloud) (http://www.ibeat.cloud/).”
[23] “We first preprocessed the MR images and extracted the cerebellum using the infant brain extraction and analysis toolbox (iBEAT V2.0 Cloud) (http://www.ibeat.cloud/).”
[24] “Brain tissue segmentation was performed using iBEAT V2.0 (http://www.ibeat.cloud) to generate brain tissue labeling maps. Each voxel was labeled as gray matter, white matter, or cerebrospinal fluid.”
[25] “To obtain high-quality meta-training labels, we segmented the preprocessed T1w images automatically by using an advanced pipeline, i.e., iBEAT. These tissue maps produced by iBEAT can be regarded as pseudo labels from the aspect of semi-supervision. To obtain accurate meta-test labels, iBEAT was first applied, followed by manual corrections by experienced neuroradiologists to produce the ground-truth tissue maps.”
[26] “All infant MR images are processed using an established infant-specific computational pipeline iBEAT V2.0 (www.ibeat.cloud) to reconstruct the cortical surfaces and generate the vertex-wise cortical features, including the surface area and the cortical thickness.”
[27] “Particularly, the segmentation task needs ground-truth segmentation maps of brain tissues for training data, and many established tools such as FSL, FreeSurfer, SPM and iBEAT can be used. In this work, we use iBEAT with careful manual verification to generate such ground-truth segmentation maps, aiming to provide more accurate segmentation.”
[28] “The final step common across analyses created a transformation into surface space. Surfaces were reconstructed from iBEAT v2.0.”
[29] “All structural images were preprocessed by a well-established infant-dedicated computational pipeline, including co-registration, intensity inhomogeneity correction, skull stripping, cerebellum removal, tissue segmentation, hemispheres separation, topological correction, and inner/middle/outer surface reconstruction.”
[30] “To generate reliable manual segmentations, we first took advantage of the follow-up 24-month scans of the same subjects, with high tissue contrast, to generate an initial automatic segmentation for 6-month scans, by using a publicly available software iBEAT (www.nitrc.org/projects/ibeat/).”
[31] “First, each T1w MR image is segmented into WM, GM, and CSF tissues by iBEAT V2.0 Cloud (http://www.ibeat.cloud).”
[32] “A second segmentation was done using the T2-weighted anatomical images, which have a better contrast between gray and white matter in young infants, using the brain extraction toolbox (Brain Extraction and Analysis Toolbox, iBEAT, v-2.0 cloud processing, https://ibeat.wildapricot.org/).”
[33] “We further validated our method on another dataset with 102 infants around 1 year of age. The cortical surfaces were reconstructed using an infant-specific pipeline, and further mapped onto the sphere.”
[34] “To improve the registration performance, we first applied the infant brain extraction and analysis toolbox (iBEAT V2.0 Cloud) to segment each scan into three tissue types and manually checked the achieved TPMs.”
[35] “All T1-weighted and T2-weighted MRIs were processed by iBEAT V2.0 Cloud (https://www.ibeat.cloud/) with the following main steps: (1) skull stripping by a learning-based method; (2) cerebellum and brainstem removal by a deep-learning-based model (densely connected U-Net); (3) intensity inhomogeneity correction by the N3 framework; (4) tissue segmentation using an age-specific deep-learning-based framework, which was visually inspected to ensure sufficient accuracy; and (5) noncortical structures filling and left/right hemisphere separation.”
[36] “All functional MRI scans were preprocessed following an infant specific procedure, including the following major steps: (1) head motion and spatial distortion correction; (2) alignment of fMRI scans onto their structural MRI scans based on tissue boundary maps.”
[37] “To determine whether infants have retinotopic organization, we first created surface reconstructions using iBEAT v2.0. These surfaces were inflated and cut to make flatmaps. The contrast between horizontal and vertical meridians was projected onto each participant’s flatmap and used for tracing visual areas. Areas were traced based on the alternations in sensitivity to horizontal and vertical meridians using a suitable protocol for adults.”
[38] “Brain tissue segmentation was first conducted to generate tissue labeling maps (gray matter, white matter, or cerebrospinal fluid) using a multi-site infant-dedicated computational toolbox, iBEAT v2.0 Cloud (http://www.ibeat.cloud).”
[39] “To provide an approximation of the regional locations of significant correlations in the voxel-based analyses, the study-specific MPF group templates were parcellated. For gray matter, the MPF templates were skull stripped, tissue segmented and parcellated using an infant-dedicated processing pipeline, iBEAT V2.0 Cloud (http://www.ibeat.cloud).”
[40] “Preprocessing, including T1w/T2w alignment, intensity inhomogeneity correction, skull stripping, and histogram matching, was performed by an infant-dedicated pipeline, iBEAT V2.0 Cloud (http://www.ibeat.cloud).”
[41] “We used T2-weighted anatomical images, which have a better contrast between gray and white matter in infants, and an independent brain extraction toolbox (Brain Extraction and Analysis Toolbox, iBEAT, v-2.0 cloud processing, https://ibeat.wildapricot.org/) to generate more accurate white and gray matter segmentations.”
[42] “MR images were quality-controlled using an automated algorithm and then preprocessed using an infant-centric processing pipeline (iBEAT v.2.0; available at https://ibeat.wildapricot.org) consisting of the following steps:(i) rigid alignment of T1w and T2w images using FLIRT; (ii) skull stripping by a learning-based method; (iii) intensity inhomogeneity correction by N3; (iv) brain tissue segmentation by an infant dedicated learning-based method; (v) hemisphere separation and subcortical filling; and (vi) topologically-correct cortical surface reconstruction.”
[43] “Brain tissue segmentation was first conducted to generate tissue labelling maps (each voxel was assigned as grey matter, white matter, or cerebrospinal fluid) using a multi-site infant-dedicated computational toolbox, iBEAT V2.0 Cloud (http://www.ibeat.cloud).”
[44] “Intensity inhomogeneity correction, skull stripping, seg-mentation and parcellation were applied sequentially to original raw intensity images, leveraging the publicly available software iBEAT V2.0 Cloud (http://www.ibeat.cloud/).”
[45] “Specifically, the 3D T1-weighted images were post-processed by the iBEAT (Infant Brain Extraction and Analysis Toolbox) V2.0 software (developed by the Developing Brain Computing lab and Baby Brain Mapping lab at the University of North Carolina at Chapel Hill). iBEAT V2.0 is a newer version of the previous iBEAT software, utilizing advanced deep learning approaches to process pediatric brain structural MRI data such as 3D T1-weighted and/or 3D T2-weighted images.”
[46] “Specifically, DICOM images were converted to NIfTI format and were postprocessed by the new iBEAT V2.0 software (Developing Brain Computing Lab and Baby Brain Mapping Lab). iBEAT V2.0 is a toolbox using advanced approaches including deep learning for processing pediatric brain T1- and T2-weighted MR images.”
[47] “All images were processed by iBEAT V2.0 Cloud (http://www.ibeat.cloud/), an infant dedicated pipeline for tissue segmentation and cortical surface reconstruction.”
[48] “The tissue segmentation maps of dHCP dataset were acquired using iBEAT V2.0 Cloud (http://www.ibeat.cloud/), with segmentation method described in (Wang et al., 2018).”
[49] “These collected MR images were processed by an infant MRI computational pipeline to extract morphological measurements of the cerebral cortex.”
[50] “A second segmentation was done using the T2-weighted anatomical images, which have a better contrast between gray and white matter in young infants, using the brain extraction toolbox (Brain Extraction and Analysis Toolbox, iBEAT, v:2.0 cloud processing, https://ibeat.wildapricot.org/).”
[51] “To generate manual segmentation, an initial segmentation was obtained with publicly available infant brain segmentation software, iBEAT.”
[52] “To generate manual segmentation for training, initial segmentation was first obtained with a publicly available infant brain segmentation software, iBEAT (http://www.nitrc.org/projects/ibeat).”
[53] “To generate the manual segmentations, we first generated an initial reasonable segmentation by using a publicly available software iBEAT (http://www.nitrc.org/projects/ibeat/).”
[54] “All MR images were preprocessed using a standard procedure.”
[55] “For each set of aligned T1, T2, and FA images, non-cerebral tissues, such as skull, brain stem and cerebellum, were removed by using iBEAT.”
[56] “To generate the ground-truth segmentations, we took a practical approach by first generating an initial reasonable segmentation by using a publicly available software iBEAT (http://www.nitrc.org/projects/ibeat/).”
[57] “Structural brain images were processed with the infant Brain Extraction and Analysis Toolbox (iBEAT) for volume-based and cortical surface-based analysis that was specifically developed for pediatric MRI scans matching the data acquisition parameters used in this study.”
[58] “We first pre-processed all images with a standard pipeline that included reorientation, resampling, intensity correction and brain extraction using iBeat developed for the neonate and infant brain.”
[59] “The data were analyzed in iBEAT, an open source toolbox for processing infant brain images.”
[60] “Individual subject’s T1- and T2-weighted images were segmented into gray matter, white matter, and CSF tissue classes by using iBEAT software (https:// www.med.unc.edu/bric/ideagroup/free-softwares/libra-longitudinal-infant-brain-processing-package), designed for neonatal and infant brain segmentation.”
[61] “All images were preprocessed with a standard pipeline in iBEAT software.”
[62] “In this study, longitudinal MRI data from healthy infant subjects are acquired and processed using UNC Infant Pipeline.”
[63] “All MR images at all the acquisition timepoints were preprocessed using an infant-specific framework.”
[64] “T2w anatomical images (hereafter referred to as T2w) were processed using iBEAT v1.”
[65] “All MR images at all the acquisition timepoints were preprocessed using a standard framework.”
[66] “The dataset was preprocessed using an infant-dedicated pipeline.”
[67] “Since it is very difficult to segment infant images accurately, especially for the 6-month-oldimages, in the preprocessing stage, we use multimodal MR images (including T1-weighted MRI, T2-weighted MRI, and DTI) and longitudinal images for multimodal longitudinal infant image segmentation, thus obtaining reasonable tissue segmentation maps of each time-point for WM, GM, and CSF.”
[68] “To obtain the “ground truth” labels, we first conducted segmentation by iBEAT, then manually corrected and modified the labels using ITK-SNAP (www.itksnap.org) under the supervision of experienced neuroscientists.”
[69] “For participants aged 0-3 years from the BCP study, we employed the officially recommended iBEAT V2.0 pipelines. This pipeline, optimized for early-age neuroimaging data preprocessing based on advanced algorithms, has shown superior performance in tissue segmentation and cortical reconstruction for BCP datasets compared to alternative approaches.”
[70] “The image resolution is isotropic 0.8 mm, and all images are bias-corrected and skull-stripped using the public infant brain extraction and analysis toolbox iBEAT V2.0 (Wang et al., 2023).”
[71] “All MR images were processed using an established structural computational pipeline (Wang et al., 2023), which includes motion and intensity inhomogeneity correction, skull stripping, tissue segmentation, reconstruction of left and right hemispheres, and inner and outer cortical surface reconstruction.”
[72] “Since Infant FreeSurfer, FreeSurfer, and iBEAT V2.0 are the encapsulated tools with fixed parameters or pretrained models specific to certain age groups, they are not retrained. We apply them directly to the testing data following their standard inference protocols.”
[73] “3D T1-weighted images were processed using the Infant Brain Extraction and Analysis Toolkit toolbox (iBEAT), which has been specifically developed for anatomical segmentation of infant brain MRI during the first year of life.”
[74] “Rs-fMRI is a powerful non-invasive tool capable of characterizing the maturation of brain functional networks, and an infant-dedicated surface-based MRI processing was used to process structural MRI and rs-fMRI data (Hu et al., 2022; F. Wang et al., 2023; L. Wang et al., 2023).”
[75] “For high-quality pseudo tissue labels during meta-training, we apply the automated iBEAT pipeline [19] to these pre-processed images. In the meta-test phase, iBEAT is similarly applied, followed by expert manual refinements to produce gold-standard annotations.”
[76] “Briefly, we used the iBEAT V2.0 toolbox to process brain structural images, reconstruct and parcellate cortical surfaces (Wang et al., 2023; Zhao et al., 2021) based on for cortical surface reconstruction, and then parcellated the cerebral cortex into 68 regions of interest following the FreeSurfer Desikan-Killiany atlas (Desikan et al., 2006).”
[77] “Image processing for both datasets included the following: (a) skull stripping using the FMRIB Software Library brain extraction tool (ZJU) and iBEAT version 2 (25) (BCP) for optimal performance.”
[78] “Due to the absence of ground-truth labels for the iSeg-19 validation set, we assess the performance of LODi through a qualitative comparison against the iBEAT V2.0 Cloud (Wang et al., 2023) model.”
[79] “Anatomical annotations for cerebrospinal fluid, gray matter, and white matter were provided by iBEAT V2.0 (Wang et al., 2023b).”
[80] “T1/T2 images were run through iBeat version 2.0 via the container available on GitHub (Wang et al., 2023), to produce segmentations for grey/white/CSF.”
[81] “T1- and T2-weighted images for each participant were rigidly aligned and tissue segmentation into gray and white matter was performed with iBEAT V2.0, followed by manual correction in ITKgray.”
[82] “Based on evaluations by clinicians and professional researchers, segmentation labels of the OASIS-3 dataset (i.e. CSF, GM, and WM) were provided by Dr. Li Wang, using the iBEAT toolbox [47].”
[83] “iBEATv2 is an integrated pipeline for infant brain analysis with comprehensive postprocessing for obtaining accurate anatomy. For a fair comparison, we only compare the brain tissue segmentation component from iBEATv2.”
[84] “All T1w and T2w MR images were processed using the iBEAT V2.0 infant-specific pipeline [41], which has been validated on over 17,000 infant MRI scans.”
[85] “A second segmentation was done using both T1- and T2-weighted anatomical images and the brain extraction toolbox (Brain Extraction and Analysis Toolbox, iBEAT, v-2.0 cloud processing, https://ibeat.wildapricot.org/).”
[86] “We processed these images using iBEAT V2.0 Cloud, an infant-specific computational pipeline (available at http://www.ibeat.cloud) (Wang et al., 2023).”
[87] “The resulting images were used to segment the different brain tissues and compartments by coupling two automatic tools dedicated to the neonatal brain: DrawEM (Developing brain Region Annotation With Expectation-Maximization) and iBEAT (infant Brain Extraction and Analysis Toolbox).”
[88] “Segmentation processing was performed using iBEAT2 (version 120 [28]). After bias correction, a first segmentation was carried out to generate the brain mask... Within this adjusted brain mask, brain tissues (grey and white matter, cortico-spinal fluid) were segmented (iBEAT2 version 205).”
[89] “To evaluate VINNA against state-of-the-art traditional segmentation methods, we further process the testing set with the docker version of the iBEAT V2.0 pipeline (Wang et al., 2023) and infantFS (Zöllei et al., 2020).”
[90] “CSF time-series was extracted using FSL FMRIB’s Automated Segmentation Tool (FAST), and white matter time-series was extracted following tissue segmentation with the Infant Brain Extraction and Analysis Toolbox (iBEAT; Li et al. 2014, 2015, 2019; Wang et al. 2023).”
[91] “Segmentation of 0–24-month-old T2w brains was performed using iBEAT (http://www.ibeat.cloud/) followed by manual correction...”
[92] “For comparison, we also present the average cortical thickness derived from the native pipeline of iBEAT V2.0...”
[93] “T1-weighted MPRAGE images of adults were segmented by the T1-multiatlas toolbox of MRICloud..., and those of infants were segmented by iBEAT V2.0 to generate gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) masks in T1 space.” Conferences/Abstracts
[94] “Cortical property maps were derived from T1- and T2-weighted MRI images, processed and registered using an infant-dedicated pipeline.”
[95] “In addition, quantitative comparisons of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) segmented from the uLF and SF images were compared to segmentations from ground truth HF images obtained by iBeat, a state-of-the-art infant brain segmentation pipeline.”
[96] “Two neuroscientists manually labeled PWML areas and corrected tissue labels generated by iBeat.”
[97] “Ground truth cortical surfaces were generated with iBEAT v2.0.”
[98] “All images were preprocessed using the infant-dedicated pipeline iBEAT V2.0 (http://www.ibeat.cloud/).”
[99] “To provide accurate brain anatomy, we perform image preprocessing and brain tissue segmentation for these MRIs to generate ground-truth segmentation of three tissues, i.e., white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF), using an in-house toolbox iBEAT with manual verification.”
[100] “Pseudo-ground truth cortical surface meshes were generated using iBEAT v2.0 for both training and performance evaluation.”
[101] “We used an infant dataset with 623 cortical surfaces, which were reconstructed via iBEAT V2.0 Cloud (http://www.ibeat.cloud/).”
[102] “Cortical surfaces were reconstructed via iBEAT V2.0 Cloud (http://www.ibeat.cloud/) and then mapped onto the sphere using FreeSurfer.”
[103] “We used minimally preprocessed data from dHCP, HCP-D, HCP-YA, and HCP-A and BCP data were preprocessed via iBEAT.”
[104] “All scans were segmented into the white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) using a learning-based method and then manually corrected by experts. All scans were firstly affinely-aligned together.”
[105] “We used an infant dataset with 864 cortical surfaces, which were reconstructed via iBEAT V2.0 Cloud (http://www.ibeat.cloud/).”
[106] “All structural and functional MR images were preprocessed following a state-of-the-art infant-tailored pipeline.”
[107] “These MR images were processed by the public infant dedicated MRI computational pipeline: iBEAT V2.0 Cloud to reconstruct cortical surfaces.”
[108] “We used a dataset with 102 pediatric subjects. The cortical surfaces were reconstructed with iBEAT V2.0 Cloud (http://www.ibeat.cloud/), an online infant-dedicated computational pipeline and then mapped onto the sphere using FreeSurfer.”
[109] “Besides T1w images and T2w images, we also employ the segmentation map and parcellation map for the diagnosis of ASD, which were generated by a publicly available software iBEAT V2.0 Cloud (http://www.ibeat.cloud).”
[110] “In preprocessing, FLIRT linear registration of other time point to 12-month-old was performed. The corresponding segmentation labels, i.e. WM, GM and hippocampus, were obtained by iBEAT toolbox and experts’ manual refinement.”
[111] “Images were analyzed with iBEAT software that enables N3 bias correction, tissue segmentation and anatomical labelling (AAL atlas).”
[112] “3D T1-weighted images with resolution of 1mm 1mm 1mm were post-processed by iBEAT V2.0 software (developed by the Developing Brain Computing lab and Baby Brain Mapping lab at the University of North Carolina at Chapel Hill) to reconstruct for cortical surface and to measure cortical thickness.”
[113] “To obtain high-quality meta-training labels, we segmented the preprocessed T1w images automatically by using an advanced pipeline, i.e., iBEAT.”
[114] “T1WI images were processed by using the UNC Infant Pipeline.”
[115] “Tissue segmentation was performed by using a deep learning method.”
[116] “Preprocessing includes tissue segmentation with iBEAT [3] and skull stripping with SynthStrip [7]. Each scan is then processed by multiple segmentation tools (e.g., SynthSeg[5], FreeSurfer [8]), producing tissue and structure labels that serve as inputs to Medley later stage.”
[117] “The cortical surfaces of each hemisphere were reconstructed using iBEAT V2.0 [25].”
[118] “The segmentation process begins with cortical surface reconstructions derived from MRI scans. These images are processed using a deep learning pipeline developed by the BRAIN lab at UNC Medical School [10].”
[119] “Second, we test the skull-stripping module of the Infant Brain Extraction and Analysis Toolbox (iBEAT) [10] developed for T1w and T2w MRI (version 2.0, release 120).”
[120] “Cortical surface maps were generated using an infant-dedicated pipeline (http://www.ibeat.cloud/) [5].”
[121] “All structural MR images were processed by a state-of-the-art infant-tailored pipeline (https://www.ibeat.cloud/) [14–17], including co-registration, intensity inhomogeneity correction, skull stripping, cerebellum removal, tissue segmentation, hemispheres separation, topological correction, and surface reconstruction.”
[122] “Each image has a resolution of 0.8 × 0.8 × 0.8 mm³ and was bias-corrected, skull-stripped, and segmented by iBEAT V2.0 [18].”
[123] “For participants aged 0–2 years, we employed the iBEAT v2.0 pipeline, which is optimized for early-age neuroimaging data and has demonstrated superior performance in tissue segmentation and cortical reconstruction for infants compared with alternative approaches.”
[124] “For infant structural and functional MRI processing, we follow the methodologies detailed in [10, 14, 15, 16] to extract the fMRI time-series for each vertex on middle cortical surfaces...”
[125] “All MR images were processed through an infant-dedicated computational pipeline, termed iBEAT V2.0.”
[126] “All images were processed with the infant dedicated computational pipeline, iBEAT V2, to reconstruct high-quality cortical surfaces...”
[127] “All T1- and T2-weighted magnetic resonance images underwent processing using an infant-specific pipeline [58]...”
[128] “We also tested various standard segmentation packages, including BrainSuite, Infant FreeSurfer, iBeat V2.0, and SynthSeg.”
[129] “MRI – T1 weighted images, averaged across right and left hemispheres, processed using the Infant Brain Extraction and Analysis Toolbox (iBEAT V2.0; Wang et al., 2023).”
[130] “Tissue segmentation and surface extraction using iBEAT v2.0.” Theses/Dissertations
[131] “In this experiment, we fed real 6-month T1w and T2w images, along with brain masks, into iBEAT to obtain segmentation outputs for WM, GM and CSF.”
[132] “An ad hoc comparison of BIBSNet to iBeat, an existing deep learning model for infant brain segmentation, was also performed.”
[1] Li, Q., et al., Spatiotemporal dynamics of the human cortical functional hierarchy across the lifespan. Nature Communications, 2026.
[2] Wang, Y., et al., Surface expansion regionalization of the hippocampus in early brain development. Developmental Cognitive Neuroscience, 2026. 79: p. 101733.
[3] Manoli, A., et al., Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavior. Nature Communications, 2026. 17(1): p. 4338.
[4] Xu, L., et al., Magnetic resonance imaging-based assessment of hippocampal glutamate and morphometric changes in preterm infants at term-equivalent age with low birth weight. Pediatric Radiology, 2026. 56(1): p. 140-149.
[5] Tung, S.S., et al., How do infant brains fold? Sulcal deepening is linked to development of sulcal span, thickness, curvature, and microstructure. Communications Biology, 2026.
[6] Turesky, T.K., et al., Longitudinal trajectories of brain development from infancy to school age and their relationship with literacy development. Proceedings of the National Academy of Sciences, 2025. 122(24): p. e2414598122.
[7] Pujol, J., et al., Unraveling the impact of prenatal air pollution for neonatal brain maturation. Environment International, 2025. 204: p. 109801.
[8] Liang, X., et al., Dissecting human cortical similarity networks across the lifespan. Neuron, 2025.
[9] Sun, L., et al., Human lifespan changes in the brain’s functional connectome. Nature Neuroscience, 2025. 28(4): p. 891-901.
[10] Sun, Y., et al., A foundation model for enhancing magnetic resonance images and downstream segmentation, registration and diagnostic tasks. Nature Biomedical Engineering, 2024.
[11] Wang, Y., et al., Surface Expansion Regionalization of the Hippocampus in Early Brain Development. bioRxiv, 2025: p. 2025.02.22.639699.
[12] Werder, E.J., et al., Early life phthalate exposure impacts gray matter and white matter volume in infants and young children. medRxiv, 2025: p. 2025.02.05.25321734.
[13] Kubota, E., et al., White matter connections of human ventral temporal cortex are organized by cytoarchitecture, eccentricity and category-selectivity from birth. Nature Human Behaviour, 2025. 9(5): p. 955-970.
[14] Shimotsuma, T., et al., Severe Bronchopulmonary Dysplasia Adversely Affects Brain Growth in Preterm Infants. Neonatology, 2024: p. 1-9.
[15] Kelly, C.E., et al., Cortical growth from infancy to adolescence in preterm and term-born children. Brain, 2024. 147(4): p. 1526-1538.
[16] Muñoz, J.S., et al., Parenting Influences on Frontal Lobe Gray Matter and Preterm Toddlers’ Problem-Solving Skills. Children, 2024. 11(2): p. 206.
[17] Chen, J.V., et al., Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset. Scientific Reports, 2024. 14(1): p. 4583.
[18] Wang, J., et al., Deep learning in pediatric neuroimaging. Displays, 2023. 80: p. 102583.
[19] Huang, Y., et al., Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age. Proc Natl Acad Sci U S A, 2022. 119(33): p. e2121748119.
[20] Sun, Y., et al., Self-supervised learning with application for infant cerebellum segmentation and analysis. Nature Communications, 2023. 14(1): p. 4717.
[21] Huang, Y., et al., Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains. The Journal of Neuroscience, 2023. 43(34): p. 6010-6020.
[22] Chen, L., et al., Four-dimensional mapping of dynamic longitudinal brain subcortical development and early learning functions in infants. Nature Communications, 2023. 14(1): p. 3727.
[23] Wang, Y., et al., Longitudinal development of the cerebellum in human infants during the first 800 days. Cell Rep, 2023. 42(4): p. 112281.
[24] Jiang, W., et al., Mapping the evolution of regional brain network efficiency and its association with cognitive abilities during the first twenty-eight months of life. Developmental Cognitive Neuroscience, 2023. 63: p. 101284.
[25] Sun, Y., et al., Dual Meta-Learning with Longitudinally Generalized Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan. arXiv preprint arXiv:2308.06774, 2023.
[26] Yin, Z., et al., Early Autism Diagnosis based on Path Signature and Siamese Unsupervised Feature Compressor. arXiv preprint arXiv:2307.06472, 2023.
[27] Zhang, L., et al., Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI. arXiv preprint arXiv:2306.11837, 2023.
[28] Ellis, C.T., et al., Movies reveal the fine-grained organization of infant visual cortex. eLife, 2025. 12: p. RP92119.
[29] Hu, D., et al., Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages. IEEE Transactions on Medical Imaging, 2020. 39(12): p. 4137-4149.
[30] Wang, L., et al., Benchmark on Automatic Six-Month-Old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE Transactions on Medical Imaging, 2019. 38(9): p. 2219-2230.
[31] Sun, Y., et al., Multi-Site Infant Brain Segmentation Algorithms: The iSeg-2019 Challenge. IEEE Transactions on Medical Imaging, 2021. 40(5): p. 1363-1376.
[32] Grotheer, M., et al., White matter myelination during early infancy is linked to spatial gradients and myelin content at birth. Nature Communications, 2022. 13(1): p. 997.
[33] Zhao, F., et al., S3Reg: Superfast Spherical Surface Registration Based on Deep Learning. IEEE Transactions on Medical Imaging, 2021. 40(8): p. 1964-1976.
[34] Chen, L., et al., A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort. NeuroImage, 2022. 253: p. 119097.
[35] Hu, D., et al., Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months. The Journal of Neuroscience, 2022. 42(3): p. 377-389.
[36] Li, Y., et al., Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction. IEEE Transactions on Medical Imaging, 2022. 41(10): p. 2764-2776.
[37] Ellis, C.T., et al., Retinotopic organization of visual cortex in human infants. Neuron, 2021. 109(16): p. 2616-2626. e6.
[38] Merhar, S.L., et al., Effects of prenatal opioid exposure on functional networks in infancy. Developmental Cognitive Neuroscience, 2021. 51: p. 100996.
[39] Corrigan, N.M., et al., Brain myelination at 7 months of age predicts later language development. NeuroImage, 2022. 263: p. 119641.
[40] Li, G., et al., Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism. Journal of Autism and Developmental Disorders, 2023. 53(6): p. 2475-2489.
[41] Natu, V.S., et al., Infants’ cortex undergoes microstructural growth coupled with myelination during development. Communications Biology, 2021. 4(1): p. 1191.
[42] Ahmad, S., et al., Multifaceted atlases of the human brain in its infancy. Nature Methods, 2023. 20(1): p. 55-64.
[43] Jiang, W., et al., Neural alterations in opioid-exposed infants revealed by edge-centric brain functional networks. Brain Communications, 2022. 4(3): p. fcac112.
[44] Gao, K., et al., Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging. Autism Research, 2021. 14(12): p. 2512-2523.
[45] Na, X., et al., Mother’s physical activity during pregnancy and newborn’s brain cortical development. Frontiers in Human Neuroscience, 2022. 16: p. 943341.
[46] Na, X., et al., Maternal Obesity during Pregnancy is Associated with Lower Cortical Thickness in the Neonate Brain. American Journal of Neuroradiology, 2021. 42(12): p. 2238-2244.
[47] Wang, Y., et al., Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years. Cerebral Cortex, 2022. 32(17): p. 3786-3798.
[48] Chen, L., et al., ABCnet: Adversarial bias correction network for infant brain MR images. Medical image analysis, 2021. 72: p. 102133.
[49] Cheng, J., et al., Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores. IEEE Trans Med Imaging, 2022. 41(7): p. 1665-1676.
[50] Grotheer, M., et al., Catch me if you can: Least myelinated white matter develops fastest during early infancy. BioRxiv, 2021: p. 2021.03. 29.437583.
[51] Zhang, W., et al., Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage, 2015. 108: p. 214-224.
[52] Nie, D., et al., 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE transactions on cybernetics, 2018. 49(3): p. 1123-1136.
[53] Wang, L., et al., LINKS: learning-based multi-source IntegratioN frameworK for Segmentation of infant brain images. Neuroimage, 2015. 108: p. 160-72.
[54] Gang, L., et al., Mapping Longitudinal Development of Local Cortical Gyrification in Infants from Birth to 2 Years of Age. The Journal of Neuroscience, 2014. 34(12): p. 4228.
[55] Li, G., et al., Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces. Neuroimage, 2014. 90: p. 266-279.
[56] Wang, L., et al., Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. NeuroImage, 2014. 89: p. 152-164.
[57] Woodburn, M., et al., The maturation and cognitive relevance of structural brain network organization from early infancy to childhood. NeuroImage, 2021. 238: p. 118232.
[58] Yamada, Y., et al., An Embodied Brain Model of the Human Foetus. Scientific Reports, 2016. 6(1): p. 27893.
[59] Lehtola, S.J., et al., Associations of age and sex with brain volumes and asymmetry in 2–5-week-old infants. Brain Structure and Function, 2019. 224(1): p. 501-513.
[60] Peyton, C., et al., White Matter Injury and General Movements in High-Risk Preterm Infants. American Journal of Neuroradiology, 2017. 38(1): p. 162-169.
[61] Zhang, Y., et al., Consistent spatial-temporal longitudinal atlas construction for developing infant brains. IEEE transactions on medical imaging, 2016. 35(12): p. 2568-2577.
[62] Adeli, E., et al., Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data. NeuroImage, 2019. 185: p. 783-792.
[63] Rekik, I., et al., Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Medical image analysis, 2016. 28: p. 1-12.
[64] Catalina Camacho, M., et al., Cerebral blood flow in 5‐to 8‐month‐olds: Regional tissue maturity is associated with infant affect. Developmental Science, 2020. 23(5): p. e12928.
[65] Rekik, I., et al., Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI. NeuroImage, 2017. 152: p. 411-424.
[66] Huang, Y., et al., Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network. Pattern Recognition, 2023: p. 109715.
[67] Wei, L., et al., Learning‐based deformable registration for infant MRI by integrating random forest with auto‐context model. Medical physics, 2017. 44(12): p. 6289-6303.
[68] Hu, S., et al., Brain deformable registration using global and local label-driven deep regression learning in the first year of life. IEEE Access, 2019. 8: p. 25691-25705.
[69] Sun, L., et al., Functional connectome through the human life span. bioRxiv, 2023: p. 2023.09.12.557193.
[70] Chen, L., et al., A longitudinally-consistent deep framework for joint subcortical segmentation and registration of infant brains. Computerized Medical Imaging and Graphics, 2026. 132: p. 102779.
[71] Ning, C., et al., Spatio-temporal reconstruction of early brain developmental trajectories via self-supervised learning. Medical Image Analysis, 2026. 112: p. 104132.
[72] Lian, Z., et al., UniSurf: Universal lifespan cortical surface reconstruction. Medical Image Analysis, 2026. 112: p. 104090.
[73] Boisgontier, J., et al., Early neurodevelopmental brain perfusion abnormalities and functional connectivity findings in infants with Prader-Willi syndrome. Journal of Neurodevelopmental Disorders, 2026. 18: p. 28.
[74] Pacyga, D.C., et al., Sex-specific associations of gestational age at birth and birth size with early life within-network brain connectivity: An exploratory study. Imaging Neuroscience, 2026. 4: p. IMAG.a.1204.
[75] Sun, Y., et al., DuMeta++: Spatiotemporal Dual Meta-Learning for Generalizable Few-Shot Brain Tissue Segmentation Across Diverse Ages. arXiv, 2026: p. 2602.07174.
[76] Engel, S.M., et al., Early life phthalate and replacement plasticizer exposures and changes in early childhood brain functional connectivity and structural morphology. Environment International, 2026. 208: p. 110119.
[77] Zhang, Y., et al., Spatiotemporal Development and Clinical Correlates of MRI-based Brain Myelination in Term- and Preterm-born Children. Radiology, 2025. 317(2): p. e251251.
[78] Nigru, A.S., et al., Rewiring Development in Brain Segmentation: Leveraging Adult Brain Priors for Enhancing Infant MRI Segmentation. arXiv, 2025: p. 2510.09306.
[79] Zhou, Y., et al., Efficient few-shot medical image segmentation via self-supervised variational autoencoder. Medical Image Analysis, 2025. 104: p. 103637.
[80] Hendrickson, T.J., et al., BIBSNet: A deep learning baby image brain segmentation network for MRI scans. Developmental Cognitive Neuroscience, 2026. 79: p. 101706.
[81] Zika, S., et al., Cortical and white matter myelination proceed in concert during early infancy. Nature Communications, 2026. 17: p. 5353.
[82] Wu, L., et al., RClaNet: An Explainable Alzheimer’s Disease Diagnosis Framework by Joint Registration and Classification. IEEE Journal of Biomedical and Health Informatics, 2024. 28(4): p. 2338-2349.
[83] Liu, J., et al., Structure-Aware Brain Tissue Segmentation for Isointense Infant MRI Data Using Multi-Phase Multi-Scale Assistance Network. IEEE Journal of Biomedical and Health Informatics, 2025. 29(2): p. 1297-1307.
[84] Yuan, X., et al., Flexible Individualized Developmental Prediction of Infant Cortical Surface Maps via Intensive Triplet Autoencoder. IEEE Transactions on Medical Imaging, 2025. 44(7): p. 3110-3122.
[85] Perez, K., et al., Hierarchical microstructural tissue growth of the gray and white matter of human visual cortex during the first year of life. Brain Structure and Function, 2026. 231: p. 15.
[86] Cheng, J., et al., STF: A spherical transformer for versatile cortical surfaces applications. NeuroImage, 2025. 318: p. 121370.
[87] Elbaz, N., et al., Investigating Cerebral Anomalies in Preterm Infants and Associated Risk Factors With Magnetic Resonance Imaging at Term-Equivalent Age. Pediatric Neurology, 2026. 175: p. 156-164.
[88] Dornier, A., et al., On the Typical Development of the Central Sulcus in Infancy: A Longitudinal Evaluation of Its Morphology and Link to Behaviour. Developmental Neuroscience, 2025.
[89] Henschel, L., et al., VINNA for neonates: Orientation independence through latent augmentations. Imaging Neuroscience, 2024. 2: p. 1-26.
[90] Guha, A., et al., Intrinsic Infant Hippocampal Function Supports Inhibitory Processing. Developmental Psychobiology, 2024. 66: p. e22529.
[91] Wu, J., et al., Age-specific optimization of the T2-weighted MRI contrast in infant and toddler brain. Magnetic Resonance in Medicine, 2025. 93: p. 1014-1025.
[92] Liu, J., et al., BrainParc: unified lifespan brain parcellation from structural magnetic resonance images. Nature Computational Science, 2026. 6: p. 588-602.
[93] Wang, Y., et al., Automatic Quality Control for Resting-State BOLD-Based Cerebrovascular Reactivity Mapping. NMR in Biomedicine, 2025. 39: p. e70208.
[94] Yuan, X., et al. Longitudinally Consistent Individualized Prediction of Infant Cortical Morphological Development. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. 2024. Cham: Springer Nature Switzerland.
[95] Tapp, A., et al. Super-Field MRI Synthesis for Infant Brains Enhanced by Dual Channel Latent Diffusion. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. 2024. Cham: Springer Nature Switzerland.
[96] Ren, Z., et al. Punctate White Matter Lesion Segmentation in Preterm Infants Powered by Counterfactually Generative Learning. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. 2023. Cham: Springer Nature Switzerland.
[97] Chen, X., et al. SurfFlow: A Flow-Based Approach for Rapid and Accurate Cortical Surface Reconstruction from Infant Brain MRI. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. 2023. Cham: Springer Nature Switzerland.
[98] Zhao, F., et al. Disentangling Site Effects with Cycle-Consistent Adversarial Autoencoder for Multi-site Cortical Data Harmonization. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. 2023. Cham: Springer Nature Switzerland.
[99] Zhang, L., et al., Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI. Med Image Comput Comput Assist Interv, 2023. 14227: p. 109-119.
[100] Chen, X., et al. Reconstruction of Cortical Surfaces with Spherical Topology from Infant Brain MRI via Recurrent Deformation Learning. 2023. arXiv:2312.05986 DOI: 10.48550/arXiv.2312.05986.
[101] Zhao, F., et al., A Deep Network for Joint Registration and Parcellation of Cortical Surfaces. Med Image Comput Comput Assist Interv, 2021. 12904: p. 171-181.
[102] Zhao, F., et al., Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks. Med Image Comput Comput Assist Interv, 2021. 12902: p. 262-272.
[103] Ahmad, S., et al. Harmonization of Multi-site Cortical Data Across the Human Lifespan. 2022. Cham: Springer Nature Switzerland.
[104] Pei, Y., et al., Learning Spatiotemporal Probabilistic Atlas of Fetal Brains with Anatomically Constrained Registration Network. Med Image Comput Comput Assist Interv, 2021. 12907: p. 239-248.
[105] Zhao, F., et al. Fast Spherical Mapping of Cortical Surface Meshes Using Deep Unsupervised Learning. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. 2022. Cham: Springer Nature Switzerland.
[106] Hu, D., et al., Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprinting. Med Image Comput Comput Assist Interv, 2020. 12267: p. 72-82.
[107] Cheng, J., et al. Spherical Transformer on Cortical Surfaces. in Machine Learning in Medical Imaging. 2022. Cham: Springer Nature Switzerland.
[108] Zhao, F., et al. Unsupervised Learning for Spherical Surface Registration. in Machine Learning in Medical Imaging. 2020. Cham: Springer International Publishing.
[109] Gao, K., et al. Informative Feature-Guided Siamese Network for Early Diagnosis of Autism. in Machine Learning in Medical Imaging. 2020. Cham: Springer International Publishing.
[110] Hu, S., et al. Infant brain deformable registration using global and local label-driven deep regression learning. in Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10. 2019. Springer.
[111] Rajasilta, O., et al., A structural volumetric connectome in infants – An MRI study within the FinnBrain Birth Cohort study, in Organization for Human Brain Mapping (OHBM). 2016: Geneva, Switzerland.
[112] Na, X., et al., Maternal Obesity during Pregnancy is Associated with Lower Cortical Thickness in the Newborn Brain, in International Society for Magnetic Resonance in Medicine. 2021: Online.
[113] Sun, Y., et al., Dual Meta-Learning with Longitudinally Consistent Regularization for One-Shot Brain Tissue Segmentation Across the Human Lifespan, in International Conference on Computer Vision (ICCV). 2023: Paris, France.
[114] Li, X., et al., Surfaces Area and Cortical Volume Development of Infant Transverse Temporal Cortex Influenced by Preterm Birth, in International Society for Magnetic Resonance in Medicine. 2019: QC, Canada.
[115] Wang, M., et al., Cortical thickness is a sensitive biomarker for characterizing the gray matter abnormities in neonates with mild white matter injury, in International Society for Magnetic Resonance in Medicine. 2019: QC, Canada.
[116] Le, I.A.T., et al., MEDLEY: Orchestrating Imperfect Segmentation Through Physics-Aware Multi-Tool Curation for Infant Brain MRI, in IEEE 23rd International Symposium on Biomedical Imaging (ISBI). 2026.
[117] Tang, K., et al., Surface-Guided Construction of 4D Volumetric Atlases of Fetal Brains, in Machine Learning in Medical Imaging: 16th International Workshop, MLMI 2025, Held in Conjunction with MICCAI 2025. 2026. Cham: Springer Nature Switzerland.
[118] Godin, C., and Holland, M., Cortical Curvedness Patterns Alter with Volumetric Expansion During Infancy: A Longitudinal Analysis, in Summer Bioengineering Conference (SBC). 2025.
[119] Kelley, W., et al., Boosting Skull-Stripping Performance for Pediatric Brain Images, in IEEE International Symposium on Biomedical Imaging (ISBI). 2024.
[120] Yuan, X., et al., Multi-task Joint Prediction of Infant Cortical Morphological and Cognitive Development, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. 2023. Cham: Springer Nature Switzerland.
[121] Cheng, J., et al., Disentangled Hybrid Transformer for Identification of Infants with Prenatal Drug Exposure, in Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. 2024. Cham: Springer Nature Switzerland.
[122] Tang, K., et al., Generation of Anatomy-Realistic 4D Infant Brain Atlases with Tissue Maps Using Generative Adversarial Networks, in 2024 IEEE International Symposium on Biomedical Imaging (ISBI). 2024.
[123] Sun, L., et al., Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories. bioRxiv, 2025.
[124] Xia, W., et al., Individualized Trajectory Prediction of Early Developing Functional Connectivity, in 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). 2025.
[125] Huang, Y., et al., Revealing Fine-grained Genetically Informed Cortical Parcellation Maps of Neonates Based on Multi-view Spectral Clustering, in 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2024.
[126] Wang, X., et al., Biophysical modeling of anatomically realistic prenatal cortical folding development. Research Square, 2026.
[127] Yuan, X., et al., Local Gradients of Functional Connectivity Enable Precise Fingerprinting of Infant Brains During Dynamic Development. bioRxiv, 2024.
[128] Rouhi, R., et al., Automated Segmentation of the Hippocampus in Pediatric Imaging, in 2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM). 2023.
[129] Barry, K., Laughlin, H., Merrill, L., and Bick, J., Integrating Dimensional Models of Early Adversity: Relative Contributions of Caregiver and Environmental Risks on Frontal-Limbic Development in Early Childhood. FLUX, 2023.
[130] Rayson, H., et al., Laminar resolution in infant MEG: Simulating event-related fields for non-invasive cortical layer inference, in BioMag. 2024.
[131] Dong, Y., Machine Learning to Uncover Neuroimaging Features of Autism. PhD thesis, King’s College London, 2025.
[132] Hendrickson, T.J., Deep Learning and State of the Art Infant Brain MRI Processing Methods. PhD dissertation, University of Minnesota, 2025.
iBEAT V2.0 is a toolbox for processing pediatric brain MR images, using multimodality (including T1w and T2w) or single-modality. The software is developed by the Developing Brain Computing Lab, and the Brain Research through Analysis and Informatics of Neuroimaging (BRAIN) Lab in the University of North Carolina at Chapel Hill. | ContactsDr. Li Wang: li_wang@med.unc.edu |