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：
10582669
负责人：
Guorong Wu
金额：
$ 33.46万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
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 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10582669
关键词：
3-Dimensional Address Affect Anecdotes Appearance Area Biological Brain Brain Diseases Brain region Cell Nucleus Cells Communities Computer Vision Systems Computer software Computing Methodologies Consumption Data Development Environment Evaluation Fluorescence Microscopy Genetic Genetic Transcription Genotype Human Image Individual Intervention Knock-out Label Lead Learning Light Link Manuals Maps Methods Microscopy Modeling Mus Neurosciences Neurosciences Research Noise Nuclear Performance Process Protocols documentation Reproducibility Resolution Sampling Science Scientist Shapes Slice Source Code Stains Structure Techniques Technology Time Tissues Training Type I DNA Topoisomerases Visual Visualization Work annotation system autism spectrum disorder biomedical imaging brain tissue cell type citizen science cloud based computerized tools contrast imaging convolutional neural network crowdsourcing deep learning design fetal flexibility generative adversarial network high resolution imaging improved microscopic imaging next generation novel programs stem cells stereoscopic success three dimensional structure tissue processing tool transfer learning two-dimensional ultra high resolution user-friendly virtual reality volunteer

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.

抽象的精确定位和表征细胞在光板荧光显微镜（LSFM）图像中的能力为对于了解整个大脑三维结构的理解，必不可少的。在我们以前的工作，我们成功地开发了一种核清除的2D核分割方法使用深度学习技术的显微镜图像。尽管卷积神经网络显示出希望在LSFM图像中的细胞分段时，我们以前的工作仅限于2D分割方案和患者从有限数量的注释数据中。在这个项目中，我们旨在开发高吞吐量3D单元格分割引擎，重点是提高细分精度和通用性。首先，我们会的使用虚拟现实（VR）和人群的强度开发基于云的半自动注释平台采购。 VR中用户友好的注释环境和立体视图可以显着改善手动注释的效率。我们设计一个半自动注释工作流程，以大大减少人类干预，从而提高不同用户注释的准确性和可复制性。在公民科学精神的启发下，我们将把注释软件扩展到人群采购平台这使我们能够在短时间内获得大量的手动注释。其次，我们将完全发展使用3D注释样品训练的3D卷积神经网络的3D细胞分割发动机。由于通常很难获得各向同性LSFM图像，因此我们将进一步开发一种超级分辨率方法估算高分辨率图像以促进3D细胞分割。第三，我们将开发转移学习使我们的3D细胞分割引擎的框架足够通用，以便应用新型LSFM数据的应用由于不同的成像设置或清除/染色协议，可能存在显着的图像外观差距。这一般框架将使我们能够快速开发具有新LSFM数据的特定单元格分段解决方案通过转移现有的3D分割引擎，很少甚至没有手动注释接受了足够数量的带注释的样品培训。第四，我们将把计算工具应用于几个试验神经科学研究：（1）研究拓扑异构酶I（自闭症链接的转录之一）调节器）调节大脑结构，（2）研究对发育中细胞类型的遗传影响通过量化胎儿皮质组织中祖细胞的数量来通过人脑。成功地执行了我们的项目将对神经科学社区产生广泛的影响，可视化和分析完整的蜂窝在健康和疾病大脑中单个细胞类型的分辨率图。改进的细胞分割引擎在3D中，来自世界各地的科学家可以准确有效地共享和处理彼此的数据，从而增加了可重复性和力量。