This package provides quality assurance / quality control scripts for FastSurfer- or FreeSurfer-processed structural MRI data.
This package provides quality assurance / quality control scripts for FastSurfer- or FreeSurfer-processed structural MRI data.
It is a revision, extension, and translation to the Python language of the Freesurfer QA Tools. It has been augmented by additional functions from the MRIQC toolbox, and with code derived from the LaPy and BrainPrint toolboxes.
This page provides general, usage, and installation information. See here for the full documentation.
The core functionality of this toolbox is to compute the following features:
| variable | description |
|---|---|
| subject | subject ID |
| wm_snr_orig | signal-to-noise ratio for white matter in orig.mgz |
| gm_snr_orig | signal-to-noise ratio for gray matter in orig.mgz |
| wm_snr_norm | signal-to-noise ratio for white matter in norm.mgz |
| gm_snr_norm | signal-to-noise ratio for gray matter in norm.mgz |
| cc_size | relative size of the corpus callosum |
| lh_holes | number of holes in the left hemisphere |
| rh_holes | number of holes in the right hemisphere |
| lh_defects | number of defects in the left hemisphere |
| rh_defects | number of defects in the right hemisphere |
| topo_lh | topological fixing time for the left hemisphere |
| topo_rh | topological fixing time for the right hemisphere |
| con_lh_snr | wm/gm contrast signal-to-noise ratio in the left hemisphere |
| con_rh_snr | wm/gm contrast signal-to-noise ratio in the right hemisphere |
| rot_tal_x | rotation component of the Talairach transform around the x axis |
| rot_tal_y | rotation component of the Talairach transform around the y axis |
| rot_tal_z | rotation component of the Talairach transform around the z axis |
The program will use an existing output directory (or try to create it) and write a csv table into that location. The csv table will contain the above metrics plus a subject identifier.
The program can also be run on images that were processed with FastSurfer
(v1.1 or later) instead of FreeSurfer. In that case, simply add a --fastsurfer
switch to your shell command. Note that FastSurfer's full processing stream must
have been run, including surface reconstruction (i.e. brain segmentation alone
is not sufficient).
In addition to the core functionality of the toolbox there are several optional modules that can be run according to need:
This module allows for the automated generation of cross-sections of the brain that are overlaid with the anatomical segmentations (asegs) and the white and pial surfaces. These images will be saved to the 'screenshots' subdirectory that will be created within the output directory. These images can be used for quickly glimpsing through the processing results. Note that no display manager is required for this module, i.e. it can be run on a remote server, for example.
This module allows for the automated generation of surface renderings of the left and right pial and inflated surfaces, overlaid with the aparc annotation. These images will be saved to the 'surfaces' subdirectory that will be created within the output directory. These images can be used for quickly glimpsing through the processing results. Note that no display manager is required for this module, i.e. it can be run on a remote server, for example.
This module allows for the automated generation cross-sections of the brain that are overlaid with the colored and semi-transparent brainmask. This allows to check the quality of the skullstripping in FreeSurfer. The resulting images will be saved to the 'skullstrip' subdirectory that will be created within the output directory.
This is a module to assess potential issues with the segmentation of the corpus callosum, which may incorrectly include parts of the fornix. To assess segmentation quality, a screenshot of the contours of the corpus callosum segmentation overlaid on the norm.mgz will be saved as 'cc.png' for each subject within the 'fornix' subdirectory of the output directory.
These modules evaluate potential missegmentations of the amygdala, hippocampus, and hypothalamus. To assess segmentation quality, screenshots will be created These modules require prior processing of the MR images with FreeSurfer's dedicated toolboxes for the segmentation of the amygdala and hippocampus, and the hypothalamus, respectively.
The shape module will run a shapeDNA / brainprint analysis to compute distances of shape descriptors between lateralized brain structures. This can be used to identify discrepancies and irregularities between pairs of corresponding structures. The results will be included in the main csv table, and the output directory will also contain a 'brainprint' subdirectory.
This is a module to detect extreme values among the subcortical ('aseg') segmentations as well as the cortical parcellations. If present, hypothalamic and hippocampal subsegmentations will also be included.
The outlier detection is based on comparisons with the distributions of the sample as well as normative values taken from the literature (see References).
For comparisons with the sample distributions, extreme values are defined in
two ways: nonparametrically, i.e. values that are 1.5 times the interquartile
range below or above the 25th or 75th percentile of the sample, respectively,
and parametrically, i.e. values that are more than 2 standard deviations above
or below the sample mean. Note that a minimum of 10 supplied subjects is
required for running these analyses, otherwise NaNs will be returned.
For comparisons with the normative values, lower and upper bounds are computed
from the 95% prediction intervals of the regression models given in Potvin et
al., 2016, and values exceeding these bounds will be flagged. As an
alternative, users may specify their own normative values by using the
'--outlier-table' argument. This requires a custom csv table with headers
label, upper, and lower, where label indicates a column of anatomical
names. It can be a subset and the order is arbitrary, but naming must exactly
match the nomenclature of the 'aseg.stats' and/or '[lr]h.aparc.stats' file.
If cortical parcellations are included in the outlier table for a comparison
with aparc.stats values, the labels must have a 'lh.' or 'rh.' prefix. upper
and lower are user-specified upper and lower bounds.
The main csv table will be appended with the following summary variables, and more detailed output about will be saved as csv tables in the 'outliers' subdirectory of the main output directory.
| variable | description |
|---|---|
| n_outliers_sample_nonpar | number of structures that are 1.5 times the IQR above/below the 75th/25th percentile |
| n_outliers_sample_param | number of structures that are 2 SD above/below the mean |
| n_outliers_norms | number of structures exceeding the upper and lower bounds of the normative values |
We are happy to announce the release of version 2.0 of the fsqc toolbox. With
this release comes a change of the project name from qatools to fsqc, to
reflect increased independence from the original FreeSurfer QA tools, and
applicability to other neuroimaging analysis packages - such as Fastsurfer.
Recent changes include the addition of the hippocampus and hypothalamus modules as well as the addition of surface and skullstrip visualization modules. Technical changes include how the package is installed, imported, and run, see below for details.
A list of changes is available here.
This repository contains multiple branches, reflecting the ongoing
development of the toolbox. The two primary branches are the main branch
(stable) and the development branch (dev). New features will first be added
to the development branch, and eventually be merged with the main branch. You
are currently on the main branch. To go to the development branch, select it
from the drop-down menu on the top left, or click here.
The goal of the fsqc project is to create a modular and extensible software
package that provides quantitative metrics and visual information for the
quality control of FreeSurfer- or Fastsurfer-processed MR images. The package
is currently under development, and new features are continuously added.
New features will initially be available in the development branch of this toolbox and will be included in the main branch after a period of testing and evaluation. Unless explicitly announced, all new features will preserve compatibility with earlier versions.
Feedback, suggestions, and contributions are always welcome, preferably via issues and pull requests.
run_fsqc --subjects_dir <directory> --output_dir <directory>
[--subjects SubjectID [SubjectID ...]]
[--subjects-file <file>] [--screenshots]
[--screenshots-html] [--surfaces] [--surfaces-html]
[--skullstrip] [--skullstrip-html]
[--fornix] [--fornix-html] [--hippocampus]
[--hippocampus-html] [--hippocampus-label ... ]
[--hypothalamus] [--hypothalamus-html] [--shape]
[--outlier] [--fastsurfer] [-h] [--more-help]
[...]
required arguments:
--subjects_dir <directory>
subjects directory with a set of Freesurfer- or
Fastsurfer-processed individual datasets.
--output_dir <directory>
output directory
optional arguments:
--subjects SubjectID [SubjectID ...]
list of subject IDs
--subjects-file <file> filename of a file with subject IDs (one per line)
--screenshots create screenshots of individual brains
--screenshots-html create screenshots of individual brains incl.
html summary page
--surfaces create screenshots of individual brain surfaces
--surfaces-html create screenshots of individual brain surfaces
and html summary page
--skullstrip create screenshots of individual brainmasks
--skullstrip-html create screenshots of individual brainmasks and
html summary page
--fornix check fornix segmentation
--fornix-html check fornix segmentation and create html summary
page of fornix evaluation
--hypothalamus check hypothalamic segmentation
--hypothalamus-html check hypothalamic segmentation and create html
summary page
--hippocampus check segmentation of hippocampus and amygdala
--hippocampus-html check segmentation of hippocampus and amygdala
and create html summary page
--hippocampus-label specify label for hippocampus segmentation files
(default: T1.v21). The full filename is then
[lr]h.hippoAmygLabels-<LABEL>.FSvoxelSpace.mgz
--shape run shape analysis
--outlier run outlier detection
--outlier-table specify normative values (only in conjunction with
--outlier)
--fastsurfer use FastSurfer instead of FreeSurfer output
--exit-on-error terminate the program when encountering an error;
otherwise, try to continue with the next module or
case
getting help:
-h, --help display this help message and exit
--more-help display extensive help message and exit
expert options:
--screenshots_base <image>
filename of an image that should be used instead of
norm.mgz as the base image for the screenshots. Can be
an individual file (which would not be appropriate for
multi-subject analysis) or can be a file without
pathname and with the same filename across subjects
within the 'mri' subdirectory of an individual
FreeSurfer results directory (which would be appropriate
for multi-subject analysis).
--screenshots_overlay <image>
path to an image that should be used instead of aseg.mgz
as the overlay image for the screenshots; can also be
none. Can be an individual file (which would not be
appropriate for multi-subject analysis) or can be a file
without pathname and with the same filename across
subjects within the 'mri' subdirectory of an individual
FreeSurfer results directory (which would be appropriate
for multi-subject analysis).
--screenshots_surf <surf> [<surf> ...]
one or more surface files that should be used instead
of [lr]h.white and [lr]h.pial; can also be none. Can be
one or more individual file(s) (which would not be
appropriate for multi-subject analysis) or can be a
(list of) file(s) without pathname and with the same
filename across subjects within the 'surf' subdirectory
of an individual FreeSurfer results directory (which
would be appropriate for multi-subject analysis).
--screenshots_views <view> [<view> ...]
one or more views to use for the screenshots in the form
of x=<numeric> y=<numeric> and/or z=<numeric>. Order
does not matter. Default views are x=-10 x=10 y=0 z=0.
--screenshots_layout <rows> <columns>
layout matrix for screenshot images.
Examples:
/my/subjects/directory:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory
/my/subjects/directory:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --subjects mySubjectID1 mySubjectID2
/my/subjects/directory after full FastSurfer processing:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --fastsurfer
/my/subjects/directory:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --screenshots
/my/subjects/directory:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --fornix
/my/subjects/directory:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --shape
/my/subjects/directory:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --outlier
/my/subjects/directory:run_fsqc --subjects_dir /my/subjects/directory --output_dir /my/output/directory --outlier --outlier-table /my/table/with/normative/values.csv
--screenshots, --fornix, --shape, and --outlier (and other) arguments can also be used in conjunction.As an alternative to their command-line usage, the fsqc scripts can also be run within a pure Python environment, i.e. installed and imported as a Python package.
Use import fsqc (or sth. equivalent) to import the package within a
Python environment, and use the run_fsqc function from the fsqc module to
run an analysis.
In its most basic form:
import fsqc
fsqc.run_fsqc(subjects_dir='/my/subjects/dir', output_dir='/my/output/dir')
Specify subjects as a list:
import fsqc
fsqc.run_fsqc(subjects_dir='/my/subjects/dir', output_dir='/my/output/dir', subjects=['subject1', 'subject2', 'subject3'])
And as a more elaborate example:
import fsqc
fsqc.run_fsqc(subjects_dir='/my/subjects/dir', output_dir='/my/output/dir', subject_file='/my/subjects/file.txt', screenshots_html=True, surfaces_html=True, skullstrip_html=True, fornix_html=True, hypothalamus_html=True, hippocampus_html=True, hippocampus_label="T1.v21", shape=True, outlier=True)
Call help(fsqc.run_fsqc) for further usage info and additional options.
We provide a configuration files that can be used to create a Docker or Singularity image for the fsqc scripts. Documentation can be found on the Docker and Singularity pages.
Use:
pip install fsqc
to install the fsqc package and all of its dependencies. This is the recommended way of installing the package, and allows for both command-line execution and execution as a Python function. We also recommend to do this installation within a Python virtual environment, which can be created and activated as follows:
virtualenv /path/to/my/virtual/environment
source /path/to/my/virtual/environment/bin/activate
Use the following code to download, build and install the fsqc package from its GitHub repository into your local Python package directory:
pip install git+https://github.com/deep-mi/fsqc.git
This can be useful if you want to install a particular branch - such as the dev
branch in the following example:
pip install git+https://github.com/deep-mi/fsqc.git@dev
This software can also be downloaded from its GitHub repository at https://github.com/Deep-MI/fsqc,
or cloned directly via git clone https://github.com/Deep-MI/fsqc.
The run_fsqc script will then be executable from the command line, as
detailed above. Note, however, that the required dependencies will have to be
installed manually. See the requirements section for
instructions.
At least one structural MR image that was processed with Freesurfer 6.0, 7.x, or FastSurfer 1.1 or later (including the surface pipeline).
A Python version >= 3.8 is required to run this script.
Required packages include (among others) the nibabel and skimage package for
the core functionality, plus the matplotlib, pandas, and transform3d
packages for some optional functions and modules. See the requirements.txt
file for a complete list. Use pip install -r requirements.txt to install
these packages.
If installing the toolbox as a Python package or if using the Docker image, all required packages will be installed automatically and manual installation as detailed above will not be necessary.
This software has been tested on Ubuntu 20.04.
A working FreeSurfer installation (version 6 or
newer) is required for running the 'shape' module of this toolbox. Also make
sure that FreeSurfer is sourced (i.e., FREESURFER_HOME is set as an
environment variable) before running an analysis.
Esteban O, Birman D, Schaer M, Koyejo OO, Poldrack RA, Gorgolewski KJ; 2017; MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites; PLOS ONE 12(9):e0184661; doi:10.1371/journal.pone.0184661.
Wachinger C, Golland P, Kremen W, Fischl B, Reuter M; 2015; BrainPrint: a Discriminative Characterization of Brain Morphology; Neuroimage: 109, 232-248; doi:10.1016/j.neuroimage.2015.01.032.
Reuter M, Wolter FE, Shenton M, Niethammer M; 2009; Laplace-Beltrami Eigenvalues and Topological Features of Eigenfunctions for Statistical Shape Analysis; Computer-Aided Design: 41, 739-755; doi:10.1016/j.cad.2009.02.007.
Potvin O, Mouiha A, Dieumegarde L, Duchesne S, & Alzheimer's Disease Neuroimaging Initiative; 2016; Normative data for subcortical regional volumes over the lifetime of the adult human brain; Neuroimage: 137, 9-20; doi.org/10.1016/j.neuroimage.2016.05.016
This software is licensed under the MIT License, see associated LICENSE file for details.
Copyright (c) 2019 Image Analysis Group, DZNE e.V.
Our BrainPrint tools provide shape descriptors of neuroanatomical structures and require a FreeSurfer or FastSurfer segmentation as a pre-processing step. BrainPrint is based on “ShapeDNA” a spectral shape descriptor.
FastSurfer is a fast and accurate deep-learning pipeline for the analysis of human brain MRI. FastSurfer provides a fully compatible FreeSurfer alternative for volumetric and surface-based thickness analysis, also supporting sub-mm resolutions, and sub-segmentation of neuroanatomical structures.
LaPy is an open-source Python package for differential geometry on triangle and tetrahedra meshes. It includes an FEM solver to estimate the Laplace, Poisson or Heat equations, also the computations of gradients, divergence, mean-curvature flow, conformal mappings, geodesics, ShapeDNA and more.