Command-line usage

This page will teach you how to use the scripts in common cases.

Converting a single-file (NIfTI) volume

If your input dataset is stored in a single file that can be read by Nibabel (such as a NIfTI file), then you are in luck. The volume-to-precomputed-pyramid script will do all the work for you. It will convert your dataset to raw pre-computed chunks.

Usage: volume-to-precomputed-pyramid [-h] [--ignore-scaling] [--mmap] [--input-min INPUT_MIN] [--input-max INPUT_MAX] [--no-gzip] [--flat] [--downscaling-method {auto,average,majority,stride}] [--outside-value OUTSIDE_VALUE] volume_filename dest_url.

You may want to use convert-chunks in a second step, to further compres your dataset with JPEG or compressed_segmentation encoding.

Converting image volumes

The instructions below are applicable to the two accepted input data layouts:

  1. Write the metadata for the full-resolution image in JSON format. If your input data is readable by Nibabel, you can use volume-to-precomputed --generate-info do do the job. Here is an example with minimal metadata (note that the resolution is expressed in nanometres):

    {
  "type": "image",
  "data_type": "uint8",
  "num_channels": 1,
  "scales": [
    {
      "size": [151, 188, 154],
      "resolution": [1000000, 1000000, 1000000],
      "voxel_offset": [0, 0, 0]
    }
  ]
}

  2. Create metadata for all scales using generate-scales-info on the previously created JSON file. This step writes a file named info in the current directory, which is needed by Neuroglancer as well as by all the subsequent steps. You are advised to create a fresh directory for each dataset.

    You can use any lossless encoding for the following steps (i.e. raw or compressed_segmentation).

    At this stage you may want to run scale-stats, which displays the number of chunks that will be created, and their uncompressed size. Thus, you can make sure that you have enough disk space before proceeding.

  3. Convert your data to raw full-resolution chunks by using one of these scripts:

    • slices-to-precomputed

    • volume-to-precomputed

  4. Compute downscaled pyramid levels using compute-scales. Make sure to use the correct downscaling method (average for greyscale images, majority for segmentations, or stride for a fast low-quality downsampling).

    At this point the raw-format data is ready to be displayed in Neuroglancer.

  1. Optionally, you can convert the raw chunks to a compressed format using convert-chunks. You will need to generate these compressed chunks in a separate directory from the raw chunks, and generate a suitable info file by using the --encoding parameter to generate-scales-info. Two compressed encodings are available:

    • compressed_segmentation: lossless compression, recommended for images containing discrete labels;

    • jpeg: lossy JPEG compression, see --jpeg-quality and --jpeg-plane.

Converting surface meshes

A surface mesh can be displayed in two ways in Neuroglancer: associated with a segmentation label as part of a segmentation type layer, or as a standalone mesh layer.

A mesh associated with a segmentation label needs to be in a Neuroglancer-specific binary precomputed format. mesh-to-precomputed can be used to convert meshes to this format. The link-mesh-fragments command must then be used so that Neuroglancer knows what meshes are associated to each label of the segmentation. See the last step of Conversion of BigBrain for an example.