DOI: 10.5061/dryad.fqz612jpc
title: Orbit Image Analysis: an open-source whole slide image analysis tool

description: 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.
description: This is a whole slide image (WSI) dataset for glomeruli
 segmentation on kidney tissue, in total 88 images.<br> The train-set
 (58 images) and test-set (32 images) has been used in the Orbit
 publication (1) to train and test the<br> glomeruli segmentation
 model (2). The images are pyramidal tiff images (tiled,
 jpeg-compression) and can be displayed with Orbit Image Analysis
 (3). The file orbit.db is a sqllite database which
 contains the manual drawn glomeruli annotations for all images, in total
 21037 annotations.<br> It can be placed in the user-home folder,
 then Orbit Image Analysis (3) will detect the database and show the
 glomeruli annotations in the annotation tab when opening an
 image.<br> (Orbit will use the md5 hashes of the images for
 identification.) For more information on how to train a
 CNN model or to use the existing model (2) please visit the Orbit deep
 learning page (4). (1) Manuel Stritt, Anna K. Stalder,
 Enrico Vezzali; Orbit Image Analysis: An open-source whole slide image
 analysis tool; Plos Computational Biology<br> (2)
 http://www.orbit.bio/deep-learning-models/<br> (3)
 http://www.orbit.bio<br> (4)
 http://www.orbit.bio/deep-learning-object-segmentation/<br> (5)
 https://creativecommons.org/share-your-work/public-domain/cc0
 Groups:<br> H&DAB Rat G1:
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