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Journal of Ophthalmology - Volume 2016 2016, Article ID 6571547, 6 pages -

Research Article

Department of Ophthalmology, University of Washington, Seattle, WA 98104, USA

Medical Retina Service, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 2PD, UK

Institute of Ophthalmology, University College London, London WC1E 6BT, UK

National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London SE1 4TT, UK

Received 5 January 2016; Accepted 19 April 2016

Academic Editor: Yannis Athanasiadis

Copyright © 2016 Aaron Y. Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Purpose. To evaluate the feasibility of using Mechanical Turk as a massively parallel platform to perform manual segmentations of macular spectral domain optical coherence tomography SD-OCT images using a MapReduce framework. Methods. A macular SD-OCT volume of 61 slice images was map-distributed to Amazon Mechanical Turk. Each Human Intelligence Task was set to 0.01 and required the user to draw five lines to outline the sublayers of the retinal OCT image after being shown example images. Each image was submitted twice for segmentation, and interrater reliability was calculated. The interface was created using custom HTML5 and JavaScript code, and data analysis was performed using R. An automated pipeline was developed to handle the map and reduce steps of the framework. Results. More than 93,500 data points were collected using this framework for the 61 images submitted. Pearson’s correlation of interrater reliability was 0.995 and coefficient of determination was 0.991. The cost of segmenting the macular volume was 1.21. A total of 22 individual Mechanical Turk users provided segmentations, each completing an average of 5.5 HITs. Each HIT was completed in an average of 4.43 minutes. Conclusions. Amazon Mechanical Turk provides a cost-effective, scalable, high-availability infrastructure for manual segmentation of OCT images.

Autor: Aaron Y. Lee, Cecilia S. Lee, Pearse A. Keane, and Adnan Tufail



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