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A supervised-learning tool for quality control of FreeSurfer segmented MRI data

Version 1.2 updated January 14 2019
Qoala-T is developed in the Brain and development research center by Lara Wierenga, PhD and Eduard Klapwijk, PhD


Qoala-T is a supervised learning tool that asseses accuracy of manual quality control of T1 imaging scans and their automated neuroanatomical labeling processed in FreeSurfer. It is particularly intended to use in developmental datasets. This package contains data and R code as described in Klapwijk et al., (2019) see The protocol of our in house developped manual QC procedure can be found here.

We have also developed an app using R Shiny by which the Qoala-T model can be run without having R installed, see the Qoala-T app.

Running Qoala-T

  • To be able to run the Qoala-T model, T1 MRI images should be processed in FreeSurfer V6.0.
  • Next extract the following output txt files using fswiki: aseg_stats.txt, aparc_thickness_lh.txt, aparc_area_lh.txt, aparc_thickness_rh.txt, aparc_area_rh.txt.
    You can also use the following script to extract only the output files necessary for Qoala-T:, an adapted version from fswiki.
  • To create an input file for the Qoala-T tool merge these files into one (see example R script).

A. Predicting scan Qoala-T score by using Braintime model

  • With this R script Qoala-T scores for a dataset are estimated using a supervised- learning model. This model is based on 784 T1-weighted imaging scans of subjects aged between 8 and 25 years old (53% females). The manual quality assessment is described in the Qoala-T manual Manual quality control procedure for structural T1 scans, also available in the supplemental material of Klapwijk et al., (2019).
  • To run the model-based Qoala-T option open Qoala_T_A_model_based_github.R and follow the instructions. Alternatively you can run this option without having R installed, see the Qoala-T app.
  • An example output table (left) and output graph (right) showing the Qoala-T score of each scan are displayed below. The figure shows the number of included and excluded predictions. The grey area represents the scans that are recommended for manual quality assesment.

B. Predicting scan Qoala-T score by rating a subset of your data

  • With this R script an in-house developed manual QC protocol can be applied on a subset of the dataset (e.g. 10%, the larger the set, the more reliable the results).
  • To run the subset-based Qoala-T option open Qoala_T_B_subset_based_github.R and follow the instructions.

    A flowchart of these processes can be observed in A and B below.

Support and communication

If you have any question or suggestion don't hesitate to get in touch. Please leave a message at the Issues page.


When using Qoala-T please include the following citation:

Klapwijk, E.T., van de Kamp, F., Meulen, M., Peters, S. and Wierenga, L.M. (2019). Qoala-T: A supervised-learning tool for quality control of FreeSurfer segmented MRI data. NeuroImage


Eduard T. Klapwijk, Ferdi van de Kamp, Mara van der Meulen, Sabine Peters, and Lara M. Wierenga

Copyright (C) 2017 Lara Wierenga - Leiden University, Brain and Development Research Center
All rights reserved