Examples

Conversion of JuBrain

In the examples/JuBrain directory of the source distribution, you will find two Nifti files based on the JuBrain human brain atlas, as published in version 2.2c of the SPM Anatomy Toolbox. Note that you need to use git-lfs in order to see the contents of the NIfTI files (otherwise you can download them from the repository on Github.)

Conversion of the grey-level template image (MNI Colin27 T1 MRI)

volume-to-precomputed \
    --generate-info \
    colin27T1_seg.nii.gz \
    colin27T1_seg/

At this point, you need to edit colin27T1_seg/info_fullres.json to set "data_type": "uint8". This is needed because colin27T1_seg.nii.gz uses a peculiar encoding, with slope and intercept set in the NIfTI header, even though only integers between 0 and 255 are encoded.

generate-scales-info colin27T1_seg/info_fullres.json colin27T1_seg/
volume-to-precomputed colin27T1_seg.nii.gz colin27T1_seg/
compute-scales colin27T1_seg/

Conversion of the Maximum Probability Map

volume-to-precomputed --generate-info MPM.nii.gz MPM/
generate-scales-info \
    --type=segmentation \
    --encoding=compressed_segmentation \
    MPM/info_fullres.json \
    MPM/
volume-to-precomputed MPM.nii.gz MPM/
compute-scales --downscaling-method=majority MPM/

Conversion of BigBrain

BigBrain is a very large image (6572 × 7404 × 5711 voxels) reconstructed from 7404 serial coronal section of a human brain, with a resolution of about 20 microns.

  1. Download slices from ftp://bigbrain.loris.ca/BigBrainRelease.2015/2D_Final_Sections/Coronal/Png/Full_Resolution/

  2. Create info_fullres.json with the appropriate metadata:

{
  "type": "image",
  "data_type": "uint8",
  "num_channels": 1,
  "scales": [
    {
      "chunk_sizes": [],
      "encoding": "raw",
      "key": "full",
      "resolution": [21166.6666666666666, 20000, 21166.6666666666666],
      "size": [6572, 7404, 5711],
      "voxel_offset": [0, 0, 0]
    }
  ]
}

  1. Create raw chunks

generate-scales-info info_fullres.json 8bit/
slices-to-precomputed --input-orientation RIA <path/to/slices> 8bit/
compute-scales --outside-value=255 8bit/

  1. Optionally, convert raw chunks to JPEG:

generate-scales-info --encoding=jpeg 8bit/info jpeg/
convert-chunks --jpeg-plane=xz 8bit/ jpeg/

  1. Convert the segmentation volume (examples/BigBrainRelease.2015/classif.nii.gz in the source distribution, this is a voxelized version of the meshes below).

volume-to-precomputed --generate-info classif.nii.gz classif/
generate-scales-info \
    --encoding=compressed_segmentation \
    classif/info_fullres.json \
    classif/
volume-to-precomputed --load-full-volume classif.nii.gz classif/
compute-scales --downscaling-method=majority classif/

  1. Add the cortical meshes to the segmentation (downloaded from ftp://bigbrain.loris.ca/BigBrainRelease.2015/3D_Surfaces/Apr7_2016/gii/). The meshes are displayed in the 3D view.

    Finally, convert the Gifti meshes to mesh fragments in pre-computed format, and create the JSON files that Neuroglancer needs in order to find the mesh fragments. The coordinate transformation is needed for two reasons:

    • the translation is the inverted transform of the classification volume (as output by volume-to-precomputed, it is needed to bring the mesh into alignment with the volume;

    • the -1 coefficients on the diagonal are needed because the X and Y axes are inverted in these Gifti files.

mesh-to-precomputed \
    --coord-transform=-1,0,0,70.7666,0,-1,0,73.01,0,0,1,58.8777 \
    --mesh-name=grey-left \
    gray_left_327680.gii \
    classif/
mesh-to-precomputed \
    --coord-transform=-1,0,0,70.7666,0,-1,0,73.01,0,0,1,58.8777 \
    --mesh-name=grey-right \
    gray_right_327680.gii \
    classif/
mesh-to-precomputed \
    --coord-transform=-1,0,0,70.7666,0,-1,0,73.01,0,0,1,58.8777 \
    --mesh-name=white-left \
    white_left_327680.gii \
    classif/
mesh-to-precomputed \
    --coord-transform=-1,0,0,70.7666,0,-1,0,73.01,0,0,1,58.8777 \
    --mesh-name=white-right \
    white_right_327680.gii \
    classif/
link-mesh-fragments --no-colon-suffix mesh_labels.csv classif/

Conversion of the grey-level template image (sharded precomputed)

volume-to-precomputed \
    --generate-info \
    --sharding 1,1,0 \
    colin27T1_seg.nii.gz \
    colin27T1_seg_sharded

At this point, you need to edit colin27T1_seg_sharded/info_fullres.json to set "data_type": "uint8". This is needed because colin27T1_seg.nii.gz uses a peculiar encoding, with slope and intercept set in the NIfTI header, even though only integers between 0 and 255 are encoded.

generate-scales-info colin27T1_seg_sharded/info_fullres.json colin27T1_seg_sharded/
volume-to-precomputed \
    --sharding 1,1,0 \
    colin27T1_seg.nii.gz \
    colin27T1_seg_sharded/
compute-scales colin27T1_seg_sharded/

Big Brain (20um) has been converted to neuroglancer precomputed format, and accessible at https://neuroglancer.humanbrainproject.eu/precomputed/BigBrainRelease.2015/8bit. Using this as the source volume, a sharded volume will be created.

mkdir sharded_bigbrain/
curl --output sharded_bigbrain/info \
  https://neuroglancer.humanbrainproject.eu/precomputed/BigBrainRelease.2015/8bit/info

At this point, sharded_bigbrain/info was edited to contain the desired sharding specification. For a smaller scale test run, 20um and 40um scales can be removed.

{
  "type": "image",
  "data_type": "uint8",
  "num_channels": 1,
  "scales": [
    {
      "chunk_sizes": [[64,64,64]],
      "encoding": "raw",
      "key": "20um",
      "resolution": [21166.6666666666666, 20000, 21166.6666666666666],
      "size": [6572, 7404, 5711],
-      "voxel_offset": [0, 0, 0]
+      "voxel_offset": [0, 0, 0],
+      "sharding": {
+         "@type": "neuroglancer_uint64_sharded_v1",
+         "data_encoding": "gzip",
+         "hash": "identity",
+         "minishard_bits": 2,
+         "minishard_index_encoding": "gzip",
+         "preshift_bits": 0,
+         "shard_bits": 2
+      }
    },
    // ...truncated for brevity
  ]
}

Start the conversion process.

convert-chunks \
  https://neuroglancer.humanbrainproject.eu/precomputed/BigBrainRelease.2015/8bit \
  ./sharded_bigbrain/