UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation

Lee, AY; Lee, CS; Keane, PA; Tufail, A; (2016) Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation. Journal of Ophthalmology , 2016 , Article 6571547. 10.1155/2016/6571547. Green open access

[thumbnail of Bracey_Tufail_6571547.pdf]
Preview
Text
Bracey_Tufail_6571547.pdf - Published Version

Download (5MB) | Preview

Abstract

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.

Type: Article
Title: Use of Mechanical Turk as a MapReduce Framework for Macular OCT Segmentation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1155/2016/6571547
Publisher version: http://dx.doi.org/10.1155/2016/6571547
Language: English
Additional information: 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 reproductio n in any medium, provided the original work is properly cited.
Keywords: Science & Technology, Life Sciences & Biomedicine, Medicine, Research & Experimental, Ophthalmology, Research & Experimental Medicine, Optical Coherence Tomography, Degeneration, Thickness, Error, Ranibizumab
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology
URI: https://discovery.ucl.ac.uk/id/eprint/1499988
Downloads since deposit
68Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item