通过计算解剖学对结构性出生缺陷小鼠模型进行 3D 定量分析的开源软件开发补充-猫眼课题宝

通过计算解剖学对结构性出生缺陷小鼠模型进行 3D 定量分析的开源软件开发补充

批准号：

10839199

负责人：

DAVID R. BEIER

金额：

$38.7万

依托单位：

SEATTLE CHILDREN'S HOSPITAL

依托单位国家：

美国

项目类别：

财政年份：

2023

资助国家：

美国

项目状态：

未结题

起止时间：

2023-09-08 至 2025-12-31

来源：

https://reporter.nih.gov/project-details/10839199

关键词：

3-Dimensional Address Adoption Anatomy Atlases Automation Biomedical Research Charge Cloud Computing Communities Complex Computer Hardware Computer Vision Systems Computer software Congenital Abnormality Contracts Data Science Data Set Detection Development Documentation Engineering Environment Fetal Development Fetus Foundations Frustration Funding Generations Genetic Goals High Performance Computing Human Image Analysis International Knockout Mice Knowledge Libraries Linux Manuals Measurement Measures Methodology Microscope Morphology Mus Operating System Organ Paper Pharmaceutical Preparations Phenotype Population Preparation Pythons Research Research Personnel Resources Sampling Science Shapes Software Engineering Software Tools Standardization Structural Congenital Anomalies Structural defect Structure Techniques Three-Dimensional Image Three-Dimensional Imaging Toxin United States National Institutes of Health Variant Visualization Work biomedical imaging cluster computing computational anatomy computer science data tools deep learning experience fetal genetic manipulation graphical user interface image registration insight interest learning strategy machine learning model microCT model organism mouse model novel open source parent grant programs response software development tool user-friendly

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中文摘要

翻译

本附录的主要目标是开发用户友好的、用户界面驱动的模块，作为开源的一部分 3D生物医学成像套件3D Slicer更容易在定量屏幕上发现新的表型已经被实验操纵过的模型生物。我们将把我们的分析管道基于在可变形图像配准和深度学习方法上，这些方法是为我们的父母资助开发的，到重新可用的通用模块。这些工具的最终用户只需具备以下方面的最低经验生物医学图像分析和/或计算机视觉概念，从而能够将工具直接应用于他们的具体问题。

英文摘要

The primary goal of this supplement is to develop user-friendly, UI driven modules as part of the open-source 3D biomedical imaging suite 3D Slicer to facilitate easier discovery of novel phenotypes in quantitative screens of model organisms that have been experimentally manipulated. We will convert our analytical pipelines based on deformable image registration and deep-learning methods, which are developed for our parent grant, into re- usable, general-purpose modules. The end-users of these tools need minimal experience with specifics of biomedical image analysis and/or computer vision concepts, thereby will be able to apply the tools directly to their specific question.

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