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.

项目摘要 脉络丛（CHP）突出到四个脑室的腔内，是 脑脊液（CSF）在神经元模式，脑生理学中共同发挥重要作用 浮力和维持体内平衡，通过提供物理，酶和免疫障碍 脑。神经影像学研究观察到CHP的形态变化，随着衰老，神经发育和 神经精神疾病，这表明CHP可能在发育和脑部疾病中发挥作用。 尽管有越来越多的证据，但卫生卫生会议并不是常用神经影像学工具的重点， 导致其细分，标签错误和错误量化的较差。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小说，快速，可靠， 可推广的，专用的开源深度学习方法，用于准确的CHP细分以了解 CHP在整个寿命中的变化以及脑部疾病之间的变化。这项研究的样本将到来 从高分辨率[人类连接项目（HCP）和相关人类疾病（CRHD）] 和常规（包括患精神病，第一事件和慢性精神病，躁郁症的高风险，以及 阿尔茨海默氏病）神经影像学数据集，总计超过22,000个大脑。拟议研究的理由 是提供一种开源细分工具，该工具将允许对CHP的角色进行更强大的分析 各种脑部疾病以及对CHP随时间变化如何随着年龄而变化的更好的基础理解。 为了实现总体目标，提出了以下三个特定目标：（1） 精确分割CHP的学习方法； （2）在整个寿命中生成CHP卷数据 可以用来表征各种纵向变化和形态差异 神经精神疾病； （3）建立可靠性，可推广性和公平性，可广泛分配 fastplex。为了实现这些目标，总共将手动分段700个大脑 - 考虑扫描仪 平衡的类型和图像解决方案，可用于年龄，性别，种族/种族，社会经济状况和脑部疾病 - 作为深度学习工具的培训，验证和测试标签。最后，可靠性和可推广性 将建立以生产将与研究界广泛分发的工具。提议 研究具有创新性和重要意义，因为它将集中于创新的全面CHP细分 工具（横向，临时喇叭，第三和第四脑室）也估计部分体积效应并提供超级 CHP标签的分辨率将共同增强有关CHP神经发育和的基础知识 神经精神病学变化。预计这项研究的结果将对 了解这些疾病的基础病理机制和发展新型策略的发展 针对特定的疾病过程。