Welcome to NeuroData Annotate’s documentation!

  1. Clone annotation repo. In Terminal type:
    git clone https://github.com/rguo123/NeuroData_Annotate.git
  2. Install dependences by typing: pip3 install -r requirements.txt
  3. Get BOSS API token by logging in to https://api.boss.neurodata.io/. Note: You need resource manager permissions to upload data to the BOSS.
  4. Create neurodata.cfg file and insert BOSS token as shown below:
protocol = https
host = api.boss.neurodata.io
  1. Pull data from BOSS by typing python3 NeuroDataResource.py in terminal and following the input prompt.
  1. Annotate with FIJI:
  1. Install onto your system using https://imagej.net/Fiji/Downloads/.
  2. Open FJII, and start a new blank TrakEM2.
  1. Navigate to the folder of your image volume and select “open”.
  2. This should have changed your ImageJ canvas. Now, drag your volume (helloworld.tif) from your folder into the canvas.
  3. In the popup window, make sure that “Resize canvas to fit stack” is checked. After clicking OK, your canvas should snap to your image.
  4. In your TrakEM2 properties, right click on “anything” in the template column and add a new “area_list”.
  1. Drag the entire “anything” folder into “Unitled 0” in the middle column.
  2. Right click the nested “anything” folder inside “Untitled 0” and add a “new area list”.
  1. Annotate Your Data by drawing all over it. You can scroll to annotate different slices in your tif.
  1. When done, right click your canvas and select “Export” -> “Arealists as labels (tif)”.

NOTE: At any point, you can export your annotations as an xml by the same method listed above. Opening the xml file will start you where you left off.

  1. A black screen will appear - these are your annotations, don’t worry if you can’t see them.
  2. Save your annotations in the correct directory with the same name, an example given below.
  1. To push annotations to the BOSS, run gen_commands.py.
  2. Paste command line output into terminal. If this doesn’t work, you will probably have to change some parameters in gen_commands.py. Below is a list of all parameters:


Parameters Description Required Tips and Examples
script Path to ingest_large_vol.py script Yes Should not have to change.
source_type Where the data is being ingested from Yes Either s3 or local.
s3_bucket_name AWS S3 Bucket name No Only specify if source_type is s3
aws_profile AWS Profile No Only specify if source_type is s3. AWS Profile Help
boss_config_file Path and filename of BOSS API token Yes Should not have to change.
slack_token Slack API token No Use if you want slack notifiction once ingest is finished.
slack_username Slack username No Use if you want slack notification once ingest is finished.
collection BOSS collection name Yes You need permission for existing BOSS collections. Specifying a collection not in the BOSS will create a new collection (again, need permissions).
experiment BOSS experiment name Yes See collection input.
channel BOSS channel name Yes See collection input.
data_directory Directory data is stored in Yes Format: Path/To/Data/
file_name Filename of data without file extension Yes Can specify which z slices you want for tif files. Example: TODO.
file_format Extension of data file Yes Example: tif, png.
z_step Increment of filename numbering Yes Typically keep at 1.
voxel_size Physical dimensions of each voxel Yes Typically keep at 1 1 1. This just determines some BOSS metadata mostly.
voxel_unit Physical unit for voxel size Yes Options: nanometers, micrometers, millimeters, centimeters.
data_type Data type of image Yes uint8 or uint16 for data, uint64 for annotations. Bug: Have to specify in ingest_large_vol.py the datatype as well.
data_dimensions X, Y, Z, dimensions of data Yes Format: X Y Z (e.g. 1280 720 5).
z_range List of z slices to ingest Yes First inclusive, last exclusive (e.g. [0, 5]).
workers Number of workers to use Yes Potential memory errors.

Dockered Approach!

  1. Install Docker by following instructions here: Docker Installation

2. In terminal, type the following command: docker pull rguo123/nddannotate

3. Start NeuroData_Annotate Container by typing in terminal docker run -it rguo123/annotate.

  1. You should now be in the docker container’s bash shell.
  2. Use the above instructions to pull data with NeuroData Resource.

6. To copy data out of the container, use the following command: docker cp CONTAINER:SRC_Path DEST_Path. Use this to copy your BOSS data onto your computer.

  1. Annotate the data following the Fiji instructions above.

8. Copy annotations back into docker container when you are ready with the command docker cp SOURCE_PATH CONTAINER:Dest_Path

9. Reenter your container with the command docker exec -it container_id /bin/bash"

  1. Run ingest_large_vol commands to successfully push your data to the BOSS.

Indices and tables