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Related Experiment Video

Updated: Jan 28, 2026

A User-friendly and Powerful R Analysis of Large-scale Datasets

A User-friendly and Powerful R Analysis of Large-scale Datasets

Published on: November 4, 2025

Large-scale medical image annotation with crowd-powered algorithms.

Eric Heim¹, Tobias Roß¹, Alexander Seitel¹

¹German Cancer Research Center (DKFZ), Computer Assisted Medical Interventions, Heidelberg, Germany.

Journal of Medical Imaging (Bellingham, Wash.)

|March 7, 2019

Summary

This summary is machine-generated.

Crowd-algorithm collaboration can effectively refine medical image segmentation. Untrained individuals achieved expert-level accuracy in detecting and correcting errors in 3D medical segmentation algorithms, proving crowdsourcing

Keywords:

crowdsourcing segmentation statistical shape models

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Area of Science:

Medical Imaging
Artificial Intelligence
Human-Computer Interaction

Background:

Accurate medical image segmentation is crucial for clinical applications.
Machine learning for segmentation requires extensive annotated data.
Current annotation methods are time-consuming and costly.

Purpose of the Study:

To evaluate untrained individuals' ability to detect and correct errors in 3D medical segmentation algorithms.
To explore crowd-algorithm collaboration for large-scale medical data annotation.
To assess the feasibility of crowdsourcing for cost-effective medical image annotation.

Main Methods:

Developed a multistage segmentation pipeline with a hybrid crowd-algorithm approach.
Integrated the algorithm into a medical imaging platform.
Conducted a pilot study using computed tomography scans for liver segmentation.

Main Results:

Untrained individuals detected and refined inaccurate organ contours with quality comparable to experts.
Crowdsourcing achieved a high annotation rate despite requiring more time per slice.
The hybrid approach demonstrated effectiveness in improving segmentation accuracy.

Conclusions:

Crowd-algorithm collaboration is a promising technique for large-scale medical data annotation.
Crowdsourcing can be a cost-effective tool for generating high-quality annotated medical images.
This approach has the potential to accelerate AI development in medical imaging.