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Related Experiment Video

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Crowdsourcing for reference correspondence generation in endoscopic images.

Lena Maier-Hein, Sven Mersmann, Daniel Kondermann

    Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
    |December 9, 2014
    PubMed
    Summary

    Crowdsourcing enables non-experts to annotate endoscopic images for computer-assisted minimally-invasive surgery (MIS). Combining multiple annotations reduces errors to levels comparable to medical experts, validating crowdsourcing for MIS image annotation.

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    Area of Science:

    • Medical Imaging
    • Computer-Assisted Surgery
    • Human-Computer Interaction

    Background:

    • Minimally-invasive surgery (MIS) relies on algorithms requiring accurate endoscopic image correspondences.
    • Expert annotations for training and validating these algorithms are scarce and time-consuming.
    • Existing public datasets lack the diversity for robust algorithm development.

    Purpose of the Study:

    • To investigate the efficacy of crowdsourcing for endoscopic video image annotation in computer-assisted MIS.
    • To determine if non-expert annotations can achieve expert-level accuracy for surgical image analysis.

    Main Methods:

    • Utilized publicly available in vivo endoscopic data.
    • Collected annotations from a large group of anonymous non-expert crowd workers.
    • Applied cluster analysis to aggregate multiple annotations for each image correspondence.

    Main Results:

    • Anonymous non-experts achieved a median annotation error of 2 pixels on 10,000 annotations.
    • Cluster analysis reduced the median error to approximately 1 pixel.
    • This accuracy level is comparable to annotations made by medical experts.

    Conclusions:

    • Crowdsourcing is a feasible and effective method for generating high-quality reference correspondences in endoscopic images.
    • This approach can significantly alleviate the bottleneck of expert annotation in computer-assisted MIS.
    • Crowdsourcing offers a scalable solution for creating diverse datasets for surgical image analysis.