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 matplotlib.pyplot as plt
import numpy as np

from dipy.core.histeq import histeq
from dipy.data import get_fnames
from dipy.io.image import load_nifti, save_nifti
from dipy.segment.mask import bounding_box, crop, median_otsu

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.

data_fnames = get_fnames(name="scil_b0")
data, affine = load_nifti(data_fnames[1])
data = np.squeeze(data)

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 In case of holes or minor errors in the mask, add the argument finalize_mask=True to apply a final step to the mask, which will fix those issues.

b0_mask, mask = median_otsu(data, median_radius=2, numpass=1)

Saving the segmentation results is very easy. 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.

fname = "se_1.5t"
save_nifti(fname + "_binary_mask.nii.gz", mask.astype(np.float32), affine)
save_nifti(fname + "_mask.nii.gz", b0_mask.astype(np.float32), affine)

Quick view of the results middle slice using matplotlib.

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")
plt.savefig(f"{fname}_median_otsu.png", bbox_inches="tight")

An application of median_otsu for brain segmentation.

We can also crop the outputs to remove the largest possible number of background voxels. This makes outputted data significantly smaller. We can use the crop function to crop the data using the bounding box of the mask.

# 1. Regenerate mask with NEW parameters (radius=4, numpass=4)
b0_mask_crop, mask_crop = median_otsu(data, median_radius=4, numpass=4)

# 2. Calculate bounding box and crop
mins, maxs = bounding_box(mask_crop)
b0_mask_crop = crop(b0_mask_crop, mins, maxs)
mask_crop = crop(mask_crop, mins, maxs)

Saving cropped data as demonstrated previously.

save_nifti(fname + "_binary_mask_crop.nii.gz", mask_crop.astype(np.float32), affine)
save_nifti(fname + "_mask_crop.nii.gz", b0_mask_crop.astype(np.float32), affine)