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Mindcontrol: A web application for brain segmentation quality control.

Anisha Keshavan1, Esha Datta1, Ian M McDonough2

  • 1Department of Neurology, University California, San Francisco, USA; UC Berkeley - UCSF Graduate Program in Bioengineering, San Francisco, USA.

Neuroimage
|April 3, 2017
PubMed
Summary
This summary is machine-generated.

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Mindcontrol is an open-source web application designed to streamline neuroimaging quality control. This tool facilitates efficient, collaborative data curation for large-scale brain imaging studies.

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Informatics

Background:

  • Accurate brain tissue classification is essential for understanding neural development, behavior, and neurological diseases.
  • Manual quality assurance of neuroimaging data is critical but challenging to scale with increasing dataset sizes.
  • Current segmentation algorithms have inherent errors requiring human oversight for reliable research and clinical translation.

Purpose of the Study:

  • To develop an efficient, open-source web application for collaborative quality control of neuroimaging processing outputs.
  • To address the challenges of manual quality assurance in large-scale neuroimaging datasets.
  • To provide a flexible platform for data curation and error correction in brain imaging research.

Main Methods:

Related Experiment Videos

  • Developed Mindcontrol, a web application featuring a data dashboard, visualizations, an imaging viewer, and an in-browser annotation/editing toolbox.
  • Configured Mindcontrol for three large, open-source datasets: 1000 Functional Connectomes Project (FCP), Consortium for Reliability and Reproducibility (CoRR), and Autism Brain Imaging Data Exchange (ABIDE).
  • Integrated quality control metrics, regional brain volumes, and thickness scalars with a 3D imaging viewer and editing module.

Main Results:

  • Mindcontrol provides a scalable and efficient protocol for quality control of neuroimaging data.
  • The platform demonstrated flexibility by configuring for diverse datasets and software outputs.
  • Demonstrated linking of quality metrics and scalar data to interactive visualization and editing tools.

Conclusions:

  • Mindcontrol offers an effective solution for large-scale, collaborative quality control in neuroimaging.
  • The application enhances the reliability of neuroimaging processing outputs for research and clinical applications.
  • Mindcontrol is adaptable to various study designs and data structures, promoting reproducible research.