Scientific Computing Biology, Idorsia Pharmaceuticals Ltd, Allschwil, Switzerland.
PLoS Comput Biol. 2020 Feb 5;16(2):e1007313. doi: 10.1371/journal.pcbi.1007313. eCollection 2020 Feb.
We describe Orbit Image Analysis, an open-source whole slide image analysis tool. The tool consists of a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or from other open-source platforms using a tile-based map-reduce execution framework. Orbit Image Analysis is capable of sophisticated whole slide imaging analyses due to several key features. First, Orbit has machine-learning capabilities. This deep learning segmentation can be integrated with complex object detection for analysis of intricate tissues. In addition, Orbit can run locally as standalone or connect to the open-source image server OMERO. Another important characteristic is its scale-out functionality, using the Apache Spark framework for distributed computing. In this paper, we describe the use of Orbit in three different real-world applications: quantification of idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in the kidney.
我们描述了 Orbit Image Analysis,这是一个开源的全玻片图像分析工具。该工具由一个通用的瓦片处理引擎组成,允许使用基于瓦片的映射-缩减执行框架执行 Orbit 本身或其他开源平台提供的各种图像分析算法。由于几个关键特性,Orbit Image Analysis 能够进行复杂的全幻灯片成像分析。首先,Orbit 具有机器学习功能。这种深度学习分割可以与复杂的物体检测集成,用于分析复杂的组织。此外,Orbit 可以作为独立的本地应用程序运行,也可以连接到开源图像服务器 OMERO。另一个重要的特点是其扩展功能,使用 Apache Spark 框架进行分布式计算。在本文中,我们描述了 Orbit 在三个不同的实际应用中的使用:特发性肺纤维化的量化、神经纤维密度的量化以及肾脏中的肾小球检测。