Dataset Information

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

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 (p < 0.0001) 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.

SUBMITTER: Lee AY

PROVIDER: S-EPMC4879255 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Lee Aaron Y AY Lee Cecilia S CS Keane Pearse A PA Tufail Adnan A

Journal of ophthalmology 20160511

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. Ea ...[more]

PMID: 27293877

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Project description:Internet interventions face significant challenges in recruitment and attrition rates are typically high and problematic. Finding innovative yet scientifically valid avenues for attaining and retaining participants is therefore of considerable importance. The main goal of this study was to compare recruitment process and participants characteristics between two similar randomized control trials of mood management interventions. One of the trials (Bunge et al., 2016) was conducted with participants recruited from Amazon's Mechanical Turk (AMT), and the other trial recruited via Unpaid Internet Resources (UIR). Methods:The AMT sample (Bunge et al., 2016) consisted of 765 adults, and the UIR sample (recruited specifically for this study) consisted of 329 adult US residents. Participants' levels of depression, anxiety, confidence, motivation, and perceived usefulness of the intervention were assessed. The AMT sample was financially compensated whereas the UIR was not. Results:AMT yielded higher recruitment rates per month (p?<?.05). At baseline, the AMT sample reported significantly lower depression and anxiety scores (p?<?.001 and p?<?.005, respectively) and significantly higher mood, motivation, and confidence (all p?<?.001) compared to the UIR sample. AMT participants spent significantly less time on the site (p?<?.05) and were more likely to complete follow-ups than the UIR sample (p?<?.05). Both samples reported a significant increase in their level of confidence and motivation from pre- to post-intervention. AMT participants showed a significant increase in perceived usefulness of the intervention (p?<?.0001), whereas the UIR sample did not (p?=?.1642). Conclusions:By using AMT, researchers can recruit very rapidly and obtain higher retention rates; however, these participants may not be representative of the general online population interested in clinical interventions. Considering that AMT and UIR participants differed in most baseline variables, data from clinical studies resulting from AMT samples should be interpreted with caution.

Dataset Information

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

Publications

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

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