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

基本信息

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
项目类别：
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&apos 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

3-Dimensional Accounting Address Adult Age Aging Alzheimer&apos 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

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项目摘要

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.