Skip to main content
Join the official 2020 Python Developers SurveyStart the survey!

BrainWeb-based multimodal models of 20 normal brains

Project description

The following example may be launched interactively via any of the following:

BrainWeb-based multimodal models of 20 normal brains

This project was initially inspired by “BrainWeb: 20 Anatomical Models of 20 Normal Brains.”

However there are a number of generally useful tools, image processing & display functions included in this project. For example, this includes volshow() for interactive comparison of multiple 3D volumes, get_file() for caching data URLs, and register() for image coregistration.


Download and Preprocessing for PET-MR Simulations

This notebook will not re-download/re-process files if they already exist.

  • Output data
    • ~/.brainweb/subject_*.npz: dtype(shape): float32(127, 344, 344)
  • Raw data source
    • ~/.brainweb/subject_*.bin.gz: dtype(shape): uint16(362, 434, 362)
  • Install
    • pip install brainweb

from __future__ import print_function, division
%matplotlib notebook
import brainweb
from brainweb import volshow
import numpy as np
from os import path
from import tqdm
import logging

Raw Data

# download
files = brainweb.get_files()

# read last file
data = brainweb.load_file(files[-1])

# show last subject
volshow(data, cmaps=['gist_ncar']);


Convert raw image data:

  • Siemens Biograph mMR resolution (~2mm) & dimensions (127, 344, 344)
  • PET/T1/T2/uMap intensities
    • PET defaults to FDG intensity ratios; could use e.g. Amyloid instead
  • randomised structure for PET/T1/T2
    • t (1 + g [2 G_sigma(r) - 1]), where
      • r = rand(127, 344, 344) in [0, 1),
      • Gaussian smoothing sigma = 1,
      • g = 1 for PET; 0.75 for MR, and
      • t = the PET or MR piecewise constant phantom
# show region probability masks
PetClass = brainweb.FDG
label_probs = brainweb.get_label_probabilities(files[-1], labels=PetClass.all_labels)
volshow(label_probs[brainweb.trim_zeros_ROI(label_probs)], titles=PetClass.all_labels, frameon=False);

for f in tqdm(files, desc="mMR ground truths", unit="subject"):
    vol = brainweb.get_mmr_fromfile(
        petNoise=1, t1Noise=0.75, t2Noise=0.75,
        petSigma=1, t1Sigma=1, t2Sigma=1,
# show last subject
volshow([vol['PET' ][:, 100:-100, 100:-100],
         vol['uMap'][:, 100:-100, 100:-100],
         vol['T1'  ][:, 100:-100, 100:-100],
         vol['T2'  ][:, 100:-100, 100:-100]],
        cmaps=['hot', 'bone', 'Greys_r', 'Greys_r'],
        titles=["PET", "uMap", "T1", "T2"],
# add some lesions
im3d = brainweb.add_lesions(vol['PET'])
volshow(im3d[:, 100:-100, 100:-100], cmaps=['hot']);
# bonus: use brute-force registration to transform
#!pip install -U 'brainweb[register]'
reg = brainweb.register(
    data[:, ::-1], target=vol['PET'],

    "PET":    vol['PET'][:, 100:-100, 100:-100],
    "RawReg": reg[       :, 100:-100, 100:-100],
    "T1":     vol['T1' ][:, 100:-100, 100:-100],
}, cmaps=['hot', 'gist_ncar', 'Greys_r'], ncols=3, tight_layout=5, figsize=(9.5, 3.5), frameon=False);

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for brainweb, version 1.6.2
Filename, size File type Python version Upload date Hashes
Filename, size brainweb-1.6.2-py2.py3-none-any.whl (11.5 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size brainweb-1.6.2.tar.gz (13.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page