Multi-Scale 3-D Image Analytics for High Dimensional Spatial Mapping of Normal Tissues
用于正常组织高维空间绘图的多尺度 3D 图像分析
基本信息
- 批准号:10251375
- 负责人:
- 金额:$ 70万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-15 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAddressAgeAgingAlgorithmic AnalysisAlgorithmic SoftwareAlgorithmsArchitectureAreaArtificial IntelligenceAtlasesBackBiological MarkersBiopsyCaliforniaCellsCellular biologyChemistryClinicalCollaborationsComputer softwareCoupledDataData CollectionDiseaseEnvironmentEnvironmental Risk FactorExposure toExtracellular MatrixFundingGenerationsGenomeGoalsGovernmentHuman BioMolecular Atlas ProgramHuman bodyImageImage AnalysisImaging technologyIndividualInstitutesLeadLinkLocationMachine LearningMapsMeasuresMethodsModelingMolecularMolecular StructureMultiomic DataNormal tissue morphologyOpticsOrganOrgan ModelOutcomePathogenicityProteomicsRNARecording of previous eventsResearchResolutionSamplingSkinSkin AgingSkin TissueSoftware ToolsSolidTechnologyThree-Dimensional ImageTimeLineTissue SampleTissue imagingTissuesTractionUV Radiation ExposureUnited States National Institutes of HealthUniversitiesVisualizationWorkage effectage groupanalysis pipelinedata integrationdata visualizationexperienceextracellularhigh dimensionalityimage visualizationimaging Segmentationimaging platforminnovationmembermultidimensional datamultidisciplinarymultiple omicsmultiplexed imagingmultiscale dataopen sourceopen source toolprogramsreconstructionsample collectionsingle cell analysissoftware developmenttask analysisthree-dimensional visualizationtomographytool
项目摘要
PROJECT SUMMARY/ABSTRACT
The overall goal of the proposed project is to develop open-source software and algorithms for 3-D reconstruc-
tion and multi-scale mapping of normal tissues. Another significant goal is to evaluate effects of aging and envi-
ronmental factors on molecular and structural architecture of skin. We will leverage our mature (TRL8) technol-
ogy for multiplexed 2-D imaging (Cell DIVE™), and our vast experience in 2-D image analytics and machine
learning. We have selected normal skin as the organ to develop these tools for several reasons, a) clinical sam-
ples from different age groups are more readily available, b) it is a good model to independently capture changes
in extracellular matrix (ECM) due to age and normal exposure to environmental factors as well as a variety of
pathogenic insults. While the ECM, cellular and intracellular molecular composition varies considerably among
various organs, we believe many of the tools developed under this program will be applicable to reconstruct and
map other organ models at high (cellular/subcellular) resolution. This proposal will focus on developing algo-
rithms and a framework for multi-scale mapping of 3-D tissue images, which will address HuBMAP priorities
around quantitative 3-D image analysis/mapping, including automated 3-D image segmentation, feature ex-
traction, and image annotation. High-resolution (subcellular) mapping of biomolecules will be implemented us-
ing 2-D multiplexed images that are used to reconstruct the 3-D tissue and linked to a lower resolution 3-D opti-
cal coherence tomography (OCT) image of the normal tissue. Other cell-level omic data (e.g., RNA FISH) will be
mapped in the same way. The low-resolution image is mapped back to a higher-level landmark (e.g., organ) as
defined by the HuBMAP common coordinate framework (CCF). As outlined, our proposed technologies will in-
clude several key features that are significant and complimentary to existing HuBMAP consortium projects and
will advance the state of the art in 3-D tissue analysis. The proposed algorithms will have several key innova-
tions that will advance the state of the art in 3-D multiplexed tissue image analysis. First, given the large vol-
umes to be analyzed, high throughput will be a key requirement of each image analysis algorithm. This will be
supported by our extensive experience in parallelizing single cell analysis pipelines. Second, the proposed algo-
rithms will segment the images at multiple scales. The third area of innovation will focus on efficient multi-
channel analysis. The proposed project will include creation of an easy-to-use software tool for assembling and
visualizing multiscale tissue data called Tissue Atlas Navigation Graphical Overview (TANGO).
项目总结/文摘
项目成果
期刊论文数量(0)
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Fiona Ginty其他文献
Fiona Ginty的其他文献
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{{ truncateString('Fiona Ginty', 18)}}的其他基金
Multiscale, Multimodal Analysis of Skin and Spatial Cell Organization
皮肤和空间细胞组织的多尺度、多模式分析
- 批准号:
10826224 - 财政年份:2022
- 资助金额:
$ 70万 - 项目类别:
Multi-Scale 3-D Image Analytics for High Dimensional Spatial Mapping of Normal Tissues
用于正常组织高维空间绘图的多尺度 3D 图像分析
- 批准号:
10246250 - 财政年份:2019
- 资助金额:
$ 70万 - 项目类别:
Systems Modeling of Tumor Heterogeneity and Therapy Response in Colorectal Cancer
结直肠癌肿瘤异质性和治疗反应的系统建模
- 批准号:
9922114 - 财政年份:2017
- 资助金额:
$ 70万 - 项目类别:
Systems Modeling of Tumor Heterogeneity and Therapy Response in Colorectal Cancer
结直肠癌肿瘤异质性和治疗反应的系统建模
- 批准号:
10174854 - 财政年份:2017
- 资助金额:
$ 70万 - 项目类别:
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