Examples¶

Here are some examples of the functionalities that one can find in the atlannot package.

Registration¶

One can compute the registration between a fixed and a moving image. Those images can be of any type (for example Atlas Annotations or simply intensity images). The inputs can be 2D or 3D, the only constraint is that they have to be of the same shape.

The main use-case of atlannot is the registration of brain volumes from one coordinate framework to another. It is then needed to allow some flexibility in terms of inputs type to accept any data such as regions annotations, intensity images.

import numpy as np

from atlannot.ants import register, transform

fixed = np.random.rand(20, 20)   # replace by a real image
moving = np.random.rand(20, 20)  # replace by a real image
# Computation of the displacement field from moving image to fixed image.
nii_data = register(fixed.astype(np.float32), moving.astype(np.float32))
# Apply the displacement to moving image.
warped = transform(moving.astype(np.float32), nii_data)

Image Manipulation¶

atlannot has also a lot of utility functions to manipulate images in order to make some pre-processing/post-processing on images.

A concrete example could be to combine a region annotation and an intensity image together and use the final result as an input to the registration. To merge information from both images, one could superpose regions borders of the annotation on top of the intensity image.

import numpy as np
from atlannot.utils import edge_laplacian_thin, merge

intensity_img = np.random.rand(20, 20) # Load intensity image here

# Create fake annotation image
annotation_img = np.zeros((20, 20))    # Load annotation image here
annotation_img[5:15, 5:15] = 1         # Load annotation image here

# Compute the borders of the annotation image
borders = edge_laplacian_thin(annotation_img)

# Merge intensity image and annotation image
merge_img = merge(intensity_img, borders)

See here other manipulation one can do on any kind of images:

import numpy as np
from atlannot.utils import (
  add_middle_line,
  edge_laplacian_thick,
  edge_laplacian_thin,
  edge_sobel,
  image_convolution,
  split_halfs,
)

# Instantiate an image
img = np.random.rand(20, 20)  # Please replace by a real image

# Apply some convolution to the image
kernel = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]
img1 = image_convolution(img, kernel=kernel)
img2 = edge_laplacian_thick(img)
img3 = edge_sobel(img)
img4 = edge_laplacian_thick(img)

# Add a middle line, can choose the axis, the tickness, ...
img5 = add_middle_line(img, axis=0, thickness=2)

# Split the image into two
half_imgs = split_halfs(img2, axis=0)[0]

Utilities¶

The atlannot contains other utilities:

Atlas utilities:
- Merge atlases to harmonize the scripts
- Unfurl regions if the regions are structured in tree
- Compute misalignments
- Remapping the labels
Notebook utilities:
- Volume Viewer to see volume in every directions
- Add colored legend to atlas images

Concrete examples¶

You can find numerous examples of the usage of atlannot package in the scripts located in the experiments directory.

git clone https://github.com/BlueBrain/atlas-annotation#egg=atlannot
cd atlas-annotation/experiments

To execute the scripts in this experiments folder, please first follow the data preparation instructions found in the Data section.

Next, one needs also to install additional packages for interactive use.

pip install git+https://github.com/BlueBrain/atlas-annotation#egg=atlannot[interactive]

Once the cloning, the installation and the download of data is done, you can use any script, for example:

python ants2d_atlas_fine.py