Brain segmentation with median_otsu

We show how to extract brain information and mask from a b0 image using DIPY’s segment.mask module.

First import the necessary modules:

import numpy as np
import nibabel as nib

Download and read the data for this tutorial.

The scil_b0 dataset contains different data from different companies and models. For this example, the data comes from a 1.5 Tesla Siemens MRI.

from import fetch_scil_b0, read_siemens_scil_b0
img = read_siemens_scil_b0()
data = np.squeeze(img.get_data())

img contains a nibabel Nifti1Image object. Data is the actual brain data as a numpy ndarray.

Segment the brain using DIPY’s mask module.

median_otsu returns the segmented brain data and a binary mask of the brain. It is possible to fine tune the parameters of median_otsu (median_radius and num_pass) if extraction yields incorrect results but the default parameters work well on most volumes. For this example, we used 2 as median_radius and 1 as num_pass

from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(data, 2, 1)

Saving the segmentation results is very easy using nibabel. We need the b0_mask, and the binary mask volumes. The affine matrix which transform the image’s coordinates to the world coordinates is also needed. Here, we choose to save both images in float32.

mask_img = nib.Nifti1Image(mask.astype(np.float32), img.affine)
b0_img = nib.Nifti1Image(b0_mask.astype(np.float32), img.affine)

fname = 'se_1.5t', fname + '_binary_mask.nii.gz'), fname + '_mask.nii.gz')

Quick view of the results middle slice using matplotlib.

import matplotlib.pyplot as plt
from dipy.core.histeq import histeq

sli = data.shape[2] // 2
plt.figure('Brain segmentation')
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(histeq(data[:, :, sli].astype('float')).T,
           cmap='gray', origin='lower')

plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(histeq(b0_mask[:, :, sli].astype('float')).T,
           cmap='gray', origin='lower')


An application of median_otsu for brain segmentation.

median_otsu can also automatically crop the outputs to remove the largest possible number of background voxels. This makes outputted data significantly smaller. Auto-cropping in median_otsu is activated by setting the autocrop parameter to True.

b0_mask_crop, mask_crop = median_otsu(data, 4, 4, autocrop=True)

Saving cropped data using nibabel as demonstrated previously.

mask_img_crop = nib.Nifti1Image(mask_crop.astype(np.float32), img.affine)
b0_img_crop = nib.Nifti1Image(
    b0_mask_crop.astype(np.float32), img.affine), fname + '_binary_mask_crop.nii.gz'), fname + '_mask_crop.nii.gz')

Example source code

You can download the full source code of this example. This same script is also included in the dipy source distribution under the doc/examples/ directory.