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婴儿开放大脑:一个婴儿脑部分割的开源库。

Baby Open Brains: An Open-Source Repository of Infant Brain Segmentations.

作者信息

Feczko Eric, Stoyell Sally M, Moore Lucille A, Alexopoulos Dimitrios, Bagonis Maria, Barrett Kenneth, Bower Brad, Cavender Addison, Chamberlain Taylor A, Conan Greg, Day Trevor Km, Goradia Dhruman, Graham Alice, Heisler-Roman Lucas, Hendrickson Timothy J, Houghton Audrey, Kardan Omid, Kiffmeyer Elizabeth A, Lee Erik G, Lundquist Jacob T, Lucena Carina, Martin Tabitha, Mummaneni Anurima, Myricks Mollie, Narnur Pranav, Perrone Anders J, Reiners Paul, Rueter Amanda R, Saw Hteemoo, Styner Martin, Sung Sooyeon, Tiklasky Barry, Wisnowski Jessica L, Yacoub Essa, Zimmermann Brett, Smyser Christopher D, Rosenberg Monica D, Fair Damien A, Elison Jed T

机构信息

Masonic Institute for the Developing Brain, University of Minnesota.

Department of Pediatrics, University of Minnesota.

出版信息

bioRxiv. 2024 Oct 14:2024.10.02.616147. doi: 10.1101/2024.10.02.616147.

Abstract

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations. Markers and expert reviewers manually segmented anatomical MRI data from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Anatomical images showed dramatic differences in myelination and intensities across the 1 to 9 month age range, emphasizing the need for densely sampled gold standard manual segmentations in these ages. The BOBs repository is publicly available through the Masonic Institute for the Developing Brain (MIDB) Open Data Initiative, which links S3 storage, Datalad for version control, and BrainBox for visualization. This repository represents an open-source paradigm, where new additions and changes can be added, enabling a community-driven resource that will improve over time and extend into new ages and protocols. These manual segmentations and the ongoing repository provide a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this repository provides a vitally needed foundation for early-life large-scale studies such as HBCD.

摘要

由于协议和处理方法高度可变,婴儿脑发育神经影像学研究的可重复性仍然有限。缺乏诸如金标准脑部分割等基准限制了可重复流程的进展。为了解决这一核心限制,我们构建了婴儿开放大脑(BOBs)存储库,这是一个开源资源,包含经过人工策划和专家审核的婴儿脑部分割。标记物和专家审核人员对来自51名参与者的71次婴儿成像检查的解剖MRI数据进行了手动分割,每次检查使用T1w和T2w图像。解剖图像显示,在1至9个月的年龄范围内,髓鞘形成和强度存在显著差异,这凸显了在这些年龄段进行密集采样的金标准手动分割的必要性。BOBs存储库可通过共济会发育中大脑研究所(MIDB)开放数据倡议公开获取,该倡议将S3存储、用于版本控制的Datalad和用于可视化的BrainBox链接起来。这个存储库代表了一种开源范式,可以添加新的内容和更改,从而形成一个由社区驱动的资源,随着时间的推移会不断改进并扩展到新的年龄和协议。这些手动分割和持续更新的存储库为评估和改进依赖于最年幼儿童群体分割的流程提供了一个基准。因此,这个存储库为诸如人类早期大脑发育研究(HBCD)等早期大规模研究提供了至关重要的基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fc1/11507744/d392c2484f78/nihpp-2024.10.02.616147v2-f0001.jpg

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