FastPlex: A Fast Deep Learning Segmentation Method for Accurate Choroid Plexus Morphometry

项目摘要 脉络丛（ChP）突出到四个脑室的内腔中，并且是脑血管的主要来源。 脑脊液（CSF），它们一起在神经元图案形成、脑生理学 浮力，并通过提供物理，酶和免疫屏障来维持体内平衡， 个脑袋神经影像学研究已经观察到ChP形态学随着年龄、神经发育和 这表明ChP可能在发育和大脑疾病中发挥作用。 尽管有越来越多的证据，但ChP并不是常用的神经成像工具的焦点， 导致其分割不良、标记错误和量化不正确。因此，迫切需要 更准确地分割ChP。该项目的总体目标是开发一种新颖，快速，可靠， 通用的，专用的开源深度学习方法，用于准确的ChP分割理解 ChP在整个生命周期中如何变化以及在大脑疾病中的差异。这项研究的样本将来自 人类连接组计划（HCP）和连接组相关人类疾病（CRHD） 和常规（包括精神病、首次发作和慢性精神病、双相情感障碍和 阿尔茨海默病）神经成像数据集，总计超过22，000个大脑。拟议研究的理由 是提供一个开源的细分工具，将允许更强大的分析到ChP的作用， 各种大脑疾病和更好地了解ChP如何随着年龄的推移而变化。 为了实现总体目标，提出了以下三个具体目标：（1）开发和验证一个深入的- 用于ChP准确分割的学习方法;（2）生成整个生命周期的ChP体积数据 它可以用来表征各种不同的纵向变化和形态差异， 神经精神疾病;（3）建立广泛分布的可靠性，普遍性和公平性 快点为了实现这些目标，总共将对700个大脑进行手动分割-考虑到扫描仪 类型和图像分辨率与年龄、性别、种族/人种、社会经济地位和脑部疾病相平衡 - 作为深度学习工具的训练、验证和测试标签。持久性、可靠性和普遍性 将建立一个工具，将广泛分发给研究界。拟议 研究是创新的和重要的，因为它将集中在一个创新的全面中国药典细分 工具（侧脑室、颞角、第三和第四脑室），也可估计部分容积效应并提供超 分辨率ChP标签，它们将共同增强关于ChP神经发育和 神经精神变化这项研究的结果预计将有助于有意义的 了解这些疾病的病理机制和发展新的战略 针对特定的疾病过程。

PROJECT SUMMARY The choroid plexus (ChP) protrudes into the lumen of the four cerebral ventricles and is the principal source of cerebrospinal fluid (CSF), which together play an important role in neuronal patterning, brain physiology, buoyancy, and maintaining homeostasis by providing physical, enzymatic, and immunological barriers to the brain. Neuroimaging studies have observed ChP morphological changes with aging, neurodevelopmental and neuropsychiatric disorders, which suggests that the ChP may play a role in development and brain disorders. Despite this growing evidence, the ChP has not been the focus of commonly used neuroimaging tools, which causes it to be poorly segmented, mislabeled, and incorrectly quantified. Therefore, there is a critical need to more accurately segment the ChP. The overall objectives for this project are to develop a novel, fast, reliable, generalizable, and dedicated open-source deep learning method for accurate ChP segmentation to understand how the ChP changes across the lifespan and differs among brain disorders. Samples for this study will come from high resolution [Human Connectome Project (HCP) and Connectome Related Human Disease (CRHD)] and conventional (inclusive of high risk for psychosis, first episode and chronic psychosis, bipolar disorder, and Alzheimer’s disease) neuroimaging datasets totaling over 22,000 brains. The rationale for the proposed research is to provide an open-source segmentation tool that will allow for more robust analyses into the ChP’s role in various brain disorders and a better foundational understanding of the how the ChP changes over time with age. To attain the overall objectives, the following three specific aims are proposed: (1) develop and validate a deep- learning method for the accurate segmentation of the ChP; (2) generate ChP volume data across the lifespan that can be used to characterize longitudinal changes and morphological differences across a variety of neuropsychiatric disorders; (3) establish reliability, generalizability, and fairness for broad distribution of FastPlex. To accomplish these aims, a total of 700 brains will be manually segmented – accounting for scanner type and image resolution that is balanced for age, sex, ethnicity/race, socioeconomic status, and brain disorder – to serve as training, validation, and testing labels for the deep-learning tool. Lasty, reliability and generalizability will be established to produce a tool that will be broadly distributed with the research community. The proposed research is innovative and significant because it will focus on an innovative comprehensive ChP segmentation tool (lateral, temporal horn, 3rd, and 4th ventricles) that also estimates partial volume effects and provides super resolution ChP labels, which together will enhance foundational knowledge on ChP neurodevelopmental and neuropsychiatric changes. The results of this research are expected to contribute meaningfully to the understanding of pathologic mechanisms underlying these disorders and to the development of novel strategies targeting specific disease processes.