AbstractWe describe the open-source whole slide image analysis tool Orbit Image Analysis. It is a generic tile-processing engine which allows the execution of various image analysis algorithms provided by either Orbit itself or other open-source solutions using a tile-based map-reduce execution framework. We show its sophisticated machine-learning approach for WSI quantification, and its flexibility by integrating a deep learning segmentation method for complex object detection. It can run locally standalone or connect to the open-source image server OMERO, and provides scale-out functionality to use the Spark framework for distributed computing. We demonstrate the application of Orbit in three real-world use-cases: Idiopathic lung fibrosis, nerve fibre density quantification, and glomeruli detection in kidney.Author summaryWhole slide images (WSI) are digital scans of samples, e.g. tissue sections. It is very convenient to view samples in this digital form, and with the increasing computation power it can also be used for quantification. These images are often too large to be analysed with standard tools. To overcome this issue, we created on open-source tool called Orbit Image Analysis which divides the images into smaller parts and allows the analysis of it with either embedded algorithms or the integration of existing tools. It also provides mechanisms to process huge amounts of images in distributed computing environments such as clusters or cloud infrastructures. In this paper we describe the Orbit system and demonstrate its application based on three real-word use-cases.
Orbit Image Analysis: An open-source whole slide image analysis tool
