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

FastPlex：一种用于精确脉络丛形态测量的快速深度学习分割方法

• 批准号: 10734956
• 负责人: Paulo L Lizano
• 金额: $ 63.85万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734956
• 关键词: 3-Dimensional; Accounting; Address; Adult; Age; Aging; Alzheimer' s Disease; American; Area; Behavioral; Bipolar Disorder; Brain; Brain Diseases; Brain scan; Cerebral Ventricles; Cerebrospinal Fluid; Chronic; Communities; Computer software; Data; Data Set; Dedications; Development; Disease; Elderly; Ensure; Ethnic Origin; Goals; Homeostasis; Horns; Human; Image; Immune; Immunologics; Individual; Inflammatory; Knowledge; Label; Lateral; Learning; Link; Longevity; Longitudinal Studies; Magnetic Resonance Imaging; Manuals; Maps; Methods; Morphology; National Institute of Mental Health; Neuroanatomy; Neurodevelopmental Disorder; Neurology; Neurosciences; Pathologic; Patients; Physiology; Play; Process; Psychiatry; Psychoses; Public Health; Race; Regulation; Research; Research Personnel; Resolution; Role; Sampling Studies; Socioeconomic Status; Source; Structure; Structure of choroid plexus; Subgroup; Techniques; Testing; Time; Tissues; Training; United States National Institutes of Health; Validation; Work; age related; cognitive function; cohort; comparison control; computerized tools; connectome; connectome data; data acquisition; deep learning; disease classification; experience; high risk; human disease; imaging biomarker; imaging capabilities; improved; innovation; interest; learning strategy; morphometry; neglect; neurodevelopment; neuroimaging; neuroimaging marker; neuroimmunology; neuronal patterning; neuropsychiatric disorder; neuropsychiatry; novel; novel strategies; open source; psychosis risk; repaired; schizophrenia spectrum disorder; secondary analysis; sex; tool; treatment strategy; ultra high resolution

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.

项目概要 脉络丛 (ChP) 伸入四个脑室的腔内，是脑电波的主要来源。 脑脊液（CSF），它们在神经元模式、脑生理学、 通过提供物理、酶和免疫屏障来维持浮力和体内平衡 神经影像学研究观察到 ChP 形态随年龄、神经发育和 神经精神疾病，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，ChP 尚未成为常用神经影像工具的焦点， 导致其分割不良、标签错误和量化错误。因此，迫切需要对其进行分类。 该项目的总体目标是开发一种新颖、快速、可靠的方法。 可推广的、专用的开源深度学习方法，用于准确的 ChP 分割以理解 ChP 在整个生命周期中如何变化以及不同脑部疾病之间的差异 本研究的样本将会出现。 来自高分辨率[人类连接组计划 (HCP) 和连接组相关人类疾病 (CRHD)] 和常规（包括精神病高风险、首发和慢性精神病、双相情感障碍和 阿尔茨海默病）神经影像数据集总计超过 22,000 个大脑。 的目的是提供一个开源细分工具，以便对 ChP 在 各种脑部疾病，以及对 ChP 如何随年龄变化的更好的基础了解。 为实现总体目标，提出以下三个具体目标：（1）开发和验证深度 (2) 生成整个生命周期的 ChP 体积数据 可用于表征各种植物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 为了实现这些目标，总共 700 个大脑将被手动分割——考虑到扫描仪。 类型和图像分辨率与年龄、性别、民族/种族、社会经济地位和大脑疾病相平衡 – 作为深度学习工具的训练、验证和测试标签，具有持久性、可靠性和通用性。 将建立一个将在研究界广泛分发的工具。 研究具有创新性且意义重大，因为它将专注于创新性的全面 ChP 细分 工具（侧脑室、颞角、第三脑室和第四脑室）还可以估计部分体积效应并提供超级 决议 ChP 标签，这些标签共同将增强 ChP 神经发育和 这项研究的结果预计将对神经精神变化做出有意义的贡献。 了解这些疾病背后的病理机制并开发新策略 针对特定的疾病过程。