Abstract
The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline (http://www.ibeat.cloud), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.
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Data availability
To quantitatively validate our pipeline, we adopted three public datasets: the BCP dataset (https://nda.nih.gov/edit_collection.html?id=2848), the dHCP dataset (http://www.developingconnectome.org/data-release/second-data-release/) and a multisite multiscanner 6-month dataset, MSMS6 (https://iseg2019.web.unc.edu/data/). The software used in this protocol can be found at www.ibeat.cloud (iBEAT V2.0 Cloud) and iBEAT V2.0 Docker version (https://github.com/iBEAT-V2/iBEAT-V2.0-Docker).
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Acknowledgements
This work was partially supported by NIH grants (MH116225, MH117943, MH109773 and MH123202). This work also utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium. The authors also thank D. Shen for an initial discussion of this work when he was with the University of North Carolina at Chapel Hill.
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L.W. and G.L. designed the pipeline framework. L.W., Z.W. and G.L. implemented the pipeline and performed the validation. Z.W., L.C. and Y.S. prepared the experimental results. L.W., Z.W., W.L. and G.L. wrote the paper.
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Nature Protocols thanks Jessica Dubois and Natasha Leporé for their contribution to the peer review of this work.
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Key references using this protocol
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Extended data
Extended Data Fig. 1 Comparison of processing results between Infant FreeSurfer and iBEAT V2.0 on different datasets.
i. Comparison on the BCP dataset. ii. Comparison on the dHCP dataset. iii. Comparison on the MSMS dataset. (a) T1w images. (b) Tissue segmentation by Infant FreeSurfer. (c) Tissue segmentation by iBEAT V2.0. (d) iBEAT V2.0 reconstructed cortical surfaces overlayed on intensity images, with red contours indicating inner surfaces and green contours indicating outer surfaces. (e) Reconstructed inner cortical surface using Infant FreeSurfer. (f) Reconstructed inner cortical surfaces using iBEAT V2.0. (g) Reconstructed outer cortical surfaces (color-coded by cortical thickness) using iBEAT V2.0.
Extended Data Fig. 2 Comparison between dHCP released results and iBEAT V2.0 processed results.
i. Comparison between the dHCP released results and the iBEAT V2.0 processed results on two randomly selected subjects in the dHCP dataset. i.(a) T2w images; i.(b) Gray matter and white matter tissue boundaries from iBEAT V2.0 segmentation (green) and the dHCP released segmentation (red); i.(c) Color-coded vertex-wise surface distance maps between iBEAT V2.0 reconstructed inner surfaces and dHCP inner surfaces; i.(d) Color-coded vertex-wise surface distance maps between our reconstructed outer surfaces and dHCP outer surfaces; i.(e) Close-up views of our reconstructed inner and outer surfaces in i.(c) and i.(d); i.(f) Close-up views of the dHCP released inner and outer surfaces in i.(c) and i.(d). ii. Quantitative comparison between iBEAT V2.0 results and the dHCP released results. ii.(a) The Dice ratio of the gray matter and white matter; ii.(b) The average surface distance (ASD, mm) of the gray matter and white matter. iii. Comparison of the reconstructed cortical surfaces (colored-coded by mean curvature) in the occipital lobe of a typical subject using the dHCP pipeline and iBEAT V2.0 pipeline. iii.(a) The posterior view. iii.(b) The medial view of the left hemisphere. iii. (c) The medial view of the right hemisphere.
Extended Data Fig. 3 Comparison of the processed results using the FastSurfer pipeline and iBEAT V2.0 pipeline on different subjects (of different ages) from the BCP dataset.
(a). T1w images. (b) FastSurfer processed results. (c) iBEAT V2.0 processed results. The green contours in the figure indicate gray matter boundaries, and the red contours indicate white matter boundaries. Since FastSurfer is mainly developed for adult brains, two comparison pipelines achieved comparable results for the 60-month-old subject.
Extended Data Fig. 4 Quantitative evaluation of different methods on datasets from different scanners/protocols.
(a) The gray matter segmentation evaluation. (b) The white matter segmentation evaluation. The Dice ratio and Average Surface Distance (ASD, mm) evaluation metrics are reported for each evaluation. The error bars denote the standard deviation of the evaluation metric using different methods on the testing subjects.
Extended Data Fig. 5 Comparison of segmentation results by typical pipelines for 5 images from the BCP dataset with and without motion artifacts.
(a) Results for images without motion artifacts. (b) Results for images with motion artifacts. (c) Difference maps of the segmentation results with and without motion artifacts. Since IFS, volBrain, FreeSurfer, and FastSurfer mainly use T1w images as input, we also only used T1w images for a fair comparison.
Extended Data Fig. 6 Typical reconstructed cortical surfaces with different levels of quality.
Illustration of the different quality levels of reconstructed cortical surfaces. The first row shows the typical surfaces that are categorized as poor quality. These surfaces have large gyri or sulci missing, causing wrong geometry and/or wrong topology. The second row shows the surfaces with fair quality. They are generally accurately reconstructed in most brain regions, with tiny gyri/sulci missing. The third row shows the good surfaces with accurately reconstructed gyri and sulci.
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Wang, L., Wu, Z., Chen, L. et al. iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat Protoc 18, 1488–1509 (2023). https://doi.org/10.1038/s41596-023-00806-x
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DOI: https://doi.org/10.1038/s41596-023-00806-x
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