A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
基本信息
- 批准号:10370398
- 负责人:
- 金额:$ 33.49万
- 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:2019-05-01 至 2024-03-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAddressAffectAnecdotesAppearanceAreaBiologicalBrainBrain DiseasesBrain regionCell NucleusCellsCommunitiesComputer Vision SystemsComputer softwareComputing MethodologiesConsumptionDataDevelopmentEnvironmentEvaluationFluorescence MicroscopyGeneticGenetic TranscriptionGenotypeGoldHumanImageIndividualInstitutesInterventionKnock-outLabelLeadLearningLightLinkManualsMapsMethodsMicroscopyModelingMusNeurosciencesNeurosciences ResearchNoiseNuclearPerformanceProcessProtocols documentationReproducibilityResolutionSamplingScienceScientistShapesSliceSource CodeStainsStructureTechniquesTechnologyTimeTissuesTrainingType I DNA TopoisomerasesVisualVisualizationWorkannotation systemautism spectrum disorderbasebiomedical imagingbrain tissuecell typecitizen sciencecloud basedcomputerized toolscontrast imagingconvolutional neural networkcrowdsourcingdeep learningdesignfetalflexibilitygenerative adversarial networkhigh resolution imagingimprovedmicroscopic imagingnext generationnovelprogramsstem cellsstereoscopicsuccessthree dimensional structuretissue processingtooltransfer learningtwo-dimensionaluser-friendlyvirtual realityvolunteer
项目摘要
Abstract
The ability of accurate localize and characterize cells in light sheet fluorescence microscopy (LSFM) image is
indispensable for shedding new light on the understanding of three dimensional structures of the whole brain. In
our previous work, we have successfully developed a 2D nuclear segmentation method for the nuclear cleared
microscopy images using deep learning techniques. Although the convolutional neural networks show promise
in segmenting cells in LSFM images, our previous work is confined in 2D segmentation scenario and suffers
from the limited number of annotated data. In this project, we aim to develop a high throughput 3D cell
segmentation engine, with the focus on improving the segmentation accuracy and generality. First, we will
develop a cloud based semi-automatic annotation platform using the strength of virtual reality (VR) and crowd
sourcing. The user-friendly annotation environment and stereoscopic view in VR can significantly improve the
efficiency of manual annotation. We design a semi-automatic annotation workflow to largely reduce human
intervention, and thus improve both the accuracy and the replicability of annotation across different users.
Enlightened by the spirit of citizen science, we will extend the annotation software into a crowd sourcing platform
which allows us to obtain a massive number of manual annotations in short time. Second, we will develop a fully
3D cell segmentation engine using 3D convolutional neural networks trained with the 3D annotated samples.
Since it is often difficult to acquire isotropic LSFM images, we will further develop a super resolution method to
impute a high resolution image to facilitate the 3D cell segmentation. Third, we will develop a transfer learning
framework to make our 3D cell segmentation engine general enough to the application of novel LSFM data which
might have significant gap of image appearance due to different imaging setup or clearing/staining protocol. This
general framework will allow us to rapidly develop a specific cell segmentation solution for new LSFM data with
very few or even no manual annotations, by transferring the existing 3D segmentation engine that has been
trained with a sufficient number of annotated samples. Fourth, we will apply our computational tools to several
pilot neuroscience studies: (1) Investigating how topoisomerase I (one of the autism linked transcriptional
regulators) regulates brain structure, and (2) Investigating genetic influence on cell types in the developing
human brain by quantifying the number of progenitor cells in fetal cortical tissue. Successful carrying out our
project will have wide-reaching impact in neuroscience community in visualizing and analyzing complete cellular
resolution maps of individual cell types within healthy and disease brain. The improved cell segmentation engine
in 3D allows scientists from all over the world to share and process each other’s data accurately and efficiently,
thus increasing reproducibility and power.
摘要
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
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专利数量(0)
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Guorong Wu其他文献
Guorong Wu的其他文献
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- 批准号:
10033069 - 财政年份:2020
- 资助金额:
$ 33.49万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
- 批准号:
10463036 - 财政年份:2019
- 资助金额:
$ 33.49万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
- 批准号:
10582669 - 财政年份:2019
- 资助金额:
$ 33.49万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
- 批准号:
10244882 - 财政年份:2019
- 资助金额:
$ 33.49万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
- 批准号:
9923760 - 财政年份:2019
- 资助金额:
$ 33.49万 - 项目类别:
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