Synthesis and Modeling of Volumetric Cell Images for Enhanced Training of Machine Learning Classifiers – An Application for Selective Plane Illumination Microscopy (SPIM) - (Project Academy “Engineering Sciences” DFG-Call No. 56)

用于增强机器学习分类器训练的体积细胞图像的合成和建模 - 选择性平面照明显微镜 (SPIM) 的应用 -（项目学院“工程科学”DFG-Call No. 56）

• 批准号: 398895982
• 负责人: Professor Dr.-Ing. Ulf-Dietrich Braumann
• 金额: --
• 依托单位: Institut für Angewandte Informatik (InfAI) e. V. - An-Institut an der Universität Leipzig
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/398895982?language=en
• 关键词: Synthesis; Modeling; Volumetric; Cell; Images

Biomedical research is demanding more and more on developments in microscopy technology to enable quantitative analysis of cell and tissue growth. Established 3D fluorescence microscopy technologies like confocal microscopy are not able to achieve these requirements due to a lack of penetration depth or photo toxic effects prohibiting the long term monitoring of living tissue. Selective Plane Illumination Microscopy (SPIM) offers a solution for this kind of problems. Automated sample positioning and image acquisition are essential for reproducibility, acquisition rate and high throughput. One goal is the implementation of machine learning algorithms and a cascaded classifier to achieve a fast and robust image analysis. The main goal is to realise of a closed loop feedback system both for sample positioning and image acquisition.

生物医学研究对显微技术的发展提出了越来越高的要求，以实现对细胞和组织生长的定量分析。已建立的3D荧光显微镜技术，如共聚焦显微镜，由于缺乏穿透深度或光毒性效应，无法实现这些要求，从而无法长期监测活组织。选择性平面照明显微镜（SPIM）为这类问题提供了解决方案。自动化样品定位和图像采集对于再现性、采集速率和高通量至关重要。一个目标是实现机器学习算法和级联分类器，以实现快速和鲁棒的图像分析。主要目标是实现一个闭环反馈系统，用于样品定位和图像采集。