NiViz Rater

Interface for rating BIDS-compliant QC outputs from NiViz.

Database: SQLite Web framework: bottle Frontend: svelte

Get started

Installation

If you prefer to use Docker please go directly to the docker section for more information! Virtualenv/Pyenv/Local installers read on ahead.

Installation of Niviz can be done, but the following requirements must be met:

1. You have Python>3.8 with pip
2. You have an installation of npm

With both of the above requirements satisfied, first clone the repository:

git clone https://github.com/jerdra/niviz-rater.git

Next, we need to build the relevant client-side bundle:

cd niviz-rater/niviz_rater/client
npm run build

This will auto-install any dependencies and build the client-side bundle to be served (CSS, JS)

Finally you can install the package using pip:

cd ../../

# You should be in the repository root directory
pip install -r requirements.txt
pip install .

This will install the niviz-rater console script that you can now use to QC your data!

How to use Niviz-Rater

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This is a general overview of how you can use NiViz-Rater with your own data. If you want to play with some sample test data check out the data/ folder where you can generate fake data and try running NiViz-Rater yourself

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Niviz-Rater was designed to be a flexible application facilitating the quality control and review process for researchers. A result of this flexibility is that the users need to tell Niviz how they want to rate their images. Researchers can talk to Niviz-Rater using a qc-specification-file which is a YAML file written in a schema that Niviz-Rater understands. In addition, NiViz-Rater expects that the user has their images named using BIDS entities to make the process of fetching files easier.

Looking directly at the schema is a little bit intimidating, instead let's work through an example. Suppose we have a set of QC images in a directory organized as follows:

qc_images
├── sub-A
│   ├── sub-A_desc-qc1_bold.svg
│   ├── sub-A_desc-qc1_T1w.svg
│   ├── sub-A_desc-qc2_bold.svg
│   └── sub-A_desc-qc2_T1w.svg
├── sub-B
│   ├── sub-B_desc-qc1_bold.svg
│   ├── sub-B_desc-qc1_T1w.svg
│   ├── sub-B_desc-qc2_bold.svg
│   └── sub-B_desc-qc2_T1w.svg
├── sub-C
│   ├── sub-C_desc-qc1_bold.svg
│   ├── sub-C_desc-qc1_T1w.svg
│   ├── sub-C_desc-qc2_bold.svg
│   └── sub-C_desc-qc2_T1w.svg
└── sub-D
    ├── sub-D_desc-qc1_bold.svg
    ├── sub-D_desc-qc1_T1w.svg
    ├── sub-D_desc-qc2_bold.svg
    └── sub-D_desc-qc2_T1w.svg

In summary, we have 2 QC images (denoted by desc-qc1 or desc-qc2) for our two modalities (_bold and _T1w). When reviewing images, the rater wants to assess the quality of the bold and T1w images; each of the qc images provides a view into each stage of whatever processing was done.

In order to tell Niviz we need to tell it:

1. What are the entities being rated - in this case it is our T1w and bold images
2. What should we use to assess each of our entities - the corresponding qc1/qc2 images

You can think of this as grouping our QC images by the actual underlying image (bold/T1w) being assessed. Now that we have an idealized layout, let's communicate it with Niviz-Rater! This is done using a qc-specification-file, let's call it my_qc.yaml:

The QC Specification File

my_qc.yaml

# Extensions that we want to search through, you can use this to ignore certain file-types
ImageExtensions: ["svg"]	

RowDescription:
	# Which BIDS entity to use to define what is a row
	entities: [subject]

	# Formatting for row-names in exportable spreadsheet
	name: "${subject}"

# Each listing in component concerns an underlying entity we want to qc (i.e bold, T1w)
Components:

	# What should the name of this QC entity be (for T1w)
	- name: "${subject} anatomical"

	# Which BIDS entities to aggregate over when generating QC views
	# and for columns in the exportable spreadsheet
	# this will make more sense with the BOLD component
	  entities: [subject]

	# What is the column name in the exportable spreadsheet	
	  column: "anatomical"

	# Which images to use to assess quality of image
	# Each entry is a key-value of BIDS entities
	  images:
	  	- { description: qc1, suffix: T1w }
	  	- { description: qc2, suffix: T1w }

	# Finally define a preset list of rating categories for T1w
	  ratings:
	  	- Incorrect MNI transformation
		- T1 surface reconstruction issue
		- Skullstrip failure
		- Good


	# For BOLD, each subject/task combination gets its own rating
	- name: "${subject} task:${task} fMRI"
	  entities: [subject, task]
	  column: "task-${task}_bold"
	  images:
	  	- {description: qc1, suffix: bold}
	  	- {description: qc2, suffix: bold}
	  ratings:
	  	- EPI-T1 mismatch
		- Bad SDC correction
		- Bad masking
		- Good

---

Note that NiViz-Rater uses pybids under the hood to pull entities. In some cases you may have BIDS entitites that are not natively recognized by pybids. Feel free to write a qc-specification-file using them for now. NiViz-Rater allows users to extend the list of valid bids entities using the --bids-settings argument

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Running NiViz-Rater

Now that you've done the hard work of defining a qc-specification-file it's time to rate our images! This is a quick two-step process:

niviz-rater -i <path_to_qc_images> -c <path_to_my_qc_yaml> \
	[--bids-settings BIDS_CONFIG_JSON ] [--db-file DB_FILE ] \
	initialize_db

niviz-rater -i <path_to_qc_images> -c <path_to_my_qc_yaml> \
	[--bids-settings BIDS_CONFIG_JSON ] [--db-file DB_FILE ]\
	runserver [--fileserver-port FILESERVER_PORT] [--port WEBSERVER_PORT]

Explanation of options:

• --bids-settings - This extends pybids with a BIDS config .json file. See pybids documentation for more info. Alteratively look at niviz_rater/data/bids.json for an example
• --db-file - Defines the output SQLite DB file, by default it will be niviz.db in the current directory. Make sure you use the same DB_FILE when you run both commands!
• --fileserver-port - NiViz-Rater spins up a simple fileserver in order to serve your QC images to the web-page, you can modify the port (default=5001) here
• --port - Port to use for NiViz-Rater's web-server

Running the runserver command will spin up a webserver you can access on your browser on localhost:5000 or localhost:<WEBSERVER_PORT> if you set --port explicitly!

Using Docker

NiViz-Rater can be run easily using Docker! First clone this repository:

git clone https://github.com/jerdra/niviz-rater.git

Next build:

# Template
docker build . -t niviz-rater[:TAG] [--rm]

# This will create niviz-rater:latest
docker build . -t niviz-rater --rm

NiViz-Rater can be used from within the container as follows:

docker run \
	[docker options...] \
	--network="host" \	# export host ports and localhost to container
	--user $UID \	# ensure that container has user permissions
	niviz-rater:[TAG]	# i.e latest
	-i /path/to/input/dataset \
	-c /path/to/yaml/file/containing/specification \
	[initialize_db, runserver] [...options]

Follow the mini-walkthrough above for more information on how to use NiViz-Rater!