Publications contributed by iBEAT

Journals [1-65] (including Neuron, Nature Methods, Nature Biomedical Engineering, Nature Human Behaviour, Nature Neuroscience, Nature Communications, PNAS, Neuroimage, Cell Reports, IEEE TMI, etc.) 

Conferences/Abstracts [66-88] (including ICCV, MICCAI, ISMRM, OHBM, etc.) 

Journals

[1] “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.”

[2] “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.”

[3] “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.”

[4] “Furthermore, we used the officially recommended iBEAT V2.0 pipelines for participants aged from 0–2 years (all from the Baby Connectome Project (BCP)).”

[5] “Tissue volumes (WM, GM, CSF, ventricle and hippocampus) and mean cortical thickness were extracted by iBEAT V2.0.”

[6] “All the scans were processed with the infant brain extraction and analysis toolbox (iBEAT V2.0) (http://www.ibeat.cloud/).”

[7] “Structural MR images were processed by iBEAT V2.0 pipeline (http://www.ibeat.cloud).”

[8] “Then, we used iBEAT V2.0 to segment the white and gray matter.”

[9] “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).”

[10] “Neonatal T1-weighted images underwent tissue segmentation using the Infant Brain Extraction and Analysis Toolbox (iBEAT) V2.0.”

[11] “The T1 and T2 scans were processed using Infant Brain Extraction and Analysis Toolbox (iBEAT V2.0 Cloud) for initial processing and brain segmentation.”  

[12] “The next best performing model (iBEATv2) yielded an average Dice of 0.949 0.017.”

[13] “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.”

[14] “For processing the BCP dataset, we used an infant dedicated computational pipeline iBEAT V2.0 (http://www.ibeat.cloud/).”

[15] “Skull stripping and cerebellum extraction were performed using an infant cerebrum-dedicated pipeline (iBEAT V2.0, http://www.ibeat.cloud). ”

[16] “All T1-weighted and T2-weighted MR images were processed using an infant-dedicated computational pipeline (http://www.ibeat.cloud/) described previously.”

[17] “All images were then preprocessed using the infant brain extraction and analysis toolbox (iBEAT V2.0 Cloud) (http://www.ibeat.cloud/).”

[18] “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/).”

[19] “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.”

[20] “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.”

[21] “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.”

[22] “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.”

[23] “The final step common across analyses created a transformation into surface space. Surfaces were reconstructed from iBEAT v2.0.”

[24] “Brain tissue segmentation from an infant-dedicated pipeline(iBEAT V2.0, http://www.ibeat.cloud) was conducted to generate brain tissue labeling maps (each voxel was labeled as gray matter, white matter, or cerebrospinal fluid).”

[25] “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.”

[26] “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/).”

[27] “First, each T1w MR image is segmented into WM, GM, and CSF tissues by iBEAT V2.0 Cloud (http://www.ibeat.cloud).”

[28] “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/).”

[29] “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.”

[30] “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.”

[31] “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.”

[32] “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.”

[33] “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.”

[34] “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).”

[35] “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).”

[36] “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).”

[37] “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.”

[38] “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.”

[39] “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).”

[40] “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/).”

[41] “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.”

[42] “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.”

[43] “All images were processed by iBEAT V2.0 Cloud (http://www.ibeat.cloud/), an infant dedicated pipeline for tissue segmentation and cortical surface reconstruction.”

[44] “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).”

[45] “These collected MR images were processed by an infant MRI computational pipeline to extract morphological measurements of the cerebral cortex.”

[46] “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/).”

[47] “To generate manual segmentation, an initial segmentation was obtained with publicly available infant brain segmentation software, iBEAT.”

[48] “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).”

[49] “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/).”

[50] “All MR images were preprocessed using a standard procedure.”

[51] “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.”

[52] “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/).”

[53] “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.”

[54] “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.”

[55] “The data were analyzed in iBEAT, an open source toolbox for processing infant brain images.

[56] “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.”

[57] “All images were preprocessed with a standard pipeline in iBEAT software.”

[58] “In this study, longitudinal MRI data from healthy infant subjects are acquired and processed using UNC Infant Pipeline.”

[59] “All MR images at all the acquisition timepoints were preprocessed using an infant-specific framework.”

[60] “T2w anatomical images (hereafter referred to as T2w) were processed using iBEAT v1.”

[61] “All MR images at all the acquisition timepoints were preprocessed using a standard framework.

[62] “The dataset was preprocessed using an infant-dedicated pipeline.”

[63] “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.”

[64] “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.”

[65] 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.”

Conferences/Abstracts

[66] “Cortical property maps were derived from T1- and T2-weighted MRI images, processed and registered using an infant-dedicated pipeline.”

[67] “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.”

[68] “Two neuroscientists manually labeled PWML areas and corrected tissue labels generated by iBeat.”

[69] “Ground truth cortical surfaces were generated with iBEAT v2.0.”

[70] “All images were preprocessed using the infant-dedicated pipeline iBEAT V2.0 (http://www.ibeat.cloud/).”

[71] “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.”

[72] “Pseudo-ground truth cortical surface meshes were generated using iBEAT v2.0 for both training and performance evaluation.”

[73] “We used an infant dataset with 623 cortical surfaces, which were reconstructed via iBEAT V2.0 Cloud (http://www.ibeat.cloud/).”

[74] “Cortical surfaces were reconstructed via iBEAT V2.0 Cloud (http://www.ibeat.cloud/) and then mapped onto the sphere using FreeSurfer.”

[75] “We used minimally preprocessed data from dHCP, HCP-D, HCP-YA, and HCP-A and BCP data were preprocessed via iBEAT.”

[76] “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.”

[77] “We used an infant dataset with 864 cortical surfaces, which were reconstructed via iBEAT V2.0 Cloud (http://www.ibeat.cloud/).”

[78] “All structural and functional MR images were preprocessed following a state-of-the-art infant-tailored pipeline.”

[79] “These MR images were processed by the public infant dedicated MRI computational pipeline: iBEAT V2.0 Cloud to reconstruct cortical surfaces.”

[80] “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.”

[81] “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).”

[82] “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.” 

[83] “Images were analyzed with iBEAT software that enables N3 bias correction, tissue segmentation and anatomical labelling (AAL atlas).”

[84] “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.” 

[85] “To obtain high-quality meta-training labels, we segmented the preprocessed T1w images automatically by using an advanced pipeline, i.e., iBEAT.”

[86] “T1WI images were processed by using the UNC Infant Pipeline.”

[87] “Tissue segmentation was performed by using a deep learning method.”

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