/snaprate

Easy snapshot-based quality control interface

Primary LanguageJavaScriptMIT LicenseMIT

snaprate

pipeline status pipeline Status DOI

Collect quality control scores from a panel of experts.

The tool works as a web application where experts can review snapshots across subjects. Users may start a session on the system using individual login/password opening a browser to the local network address where the server is running.

Demo

http://snaprate.herokuapp.com (user/password: guest/guest)

Usage

Once the server is running, users may log in using their attributed credentials to start their review from the main screen:

screenshot2.png screenshot3.png

  • Buttons previous subject/next subject are used to navigate through the different snapshots. As a result, the subject's identifier gets incremented/decremented. Any given ID may be accessed through direct URL: <host>:<port>?id=<ID> (ex: localhost:8888?id=42).

  • Button Your score is for the user to review the current snapshot. Clicking on it multiple times would turn it from not rated to green, then red, then orange, then back to not rated. The text box located just under this button is for potential comments. To save your review, just move on to the following/previous subject.

  • Each snapshot comes with an automatic prediction for quality, reflected by the color (green or red) of the Automatic prediction button. The link located on the left next to this button allows to skip to the next predicted failed case. On the right is displayed information regarding volumes of gray and white matter.

  • User reviews are systematically and automatically stored server-side. Nevertheless, button download allows to save them locally in an Excel table.

Setup

  • Server-side:

    • place a collection of snapshots in $PATH/web/data/ (Note: follow the structure of the default folder provided)
    • run the web server (python $PATH/snaprate/server.py)

  • Client-side: open a browser pointing to the server address (and defined port (default:8890))

Note: this code was initially written to allow comparisons across different methods over a group of subjects. For this kind of application, please refer to the branch named hipposeg_comparison.

Dependencies

  • tornado
  • pandas

Live action

liveaction