ANTsPyT1w

reference processing for t1-weighted neuroimages (human)

the outputs of these processes can be used for data inspection/cleaning/triage as well for interrogating neuroscientific hypotheses.

this package also keeps track of the latest preferred algorithm variations for production environments.

install by calling (within the source directory):

python setup.py install

or install via pip install antspyt1w

what this will do

• provide example data

• brain extraction

• denoising

• n4 bias correction

• brain parcellation into tissues, hemispheres, lobes and regions

• hippocampus specific segmentation

• t1 hypointensity segmentation and classification exploratory

• deformable registration with robust and repeatable parameters

• registration-based labeling of major white matter tracts

• helpers that organize and annotate segmentation variables into data frames

• hypothalamus segmentation FIXME/TODO

the two most time-consuming processes are hippocampus-specific segentation (because it uses augmentation) and registration. both take 10-20 minutes depending on your available computational resources and the data. both could be made computationally cheaper at the cost of accuracy/reliability.

first time setup

import antspyt1w
antspyt1w.get_data()

NOTE: get_data has a force_download option to make sure the latest package data is installed.

example processing

import os
os.environ["TF_NUM_INTEROP_THREADS"] = "8"
os.environ["TF_NUM_INTRAOP_THREADS"] = "8"
os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "8"

import antspyt1w
import antspynet
import ants

##### get example data + reference templates
# NOTE:  PPMI-3803-20120814-MRI_T1-I340756 is a good example of our naming style
# Study-SubjectID-Date-Modality-UniqueID
# where Modality could also be measurement or something else
fn = antspyt1w.get_data('PPMI-3803-20120814-MRI_T1-I340756', target_extension='.nii.gz' )
img = ants.image_read( fn )

# generalized default processing
myresults = antspyt1w.hierarchical( img, output_prefix = '/tmp/XXX' )

##### organize summary data into data frames - user should pivot these to columns
# and attach to unique IDs when accumulating for large-scale studies
# see below for how to easily pivot into wide format
# https://stackoverflow.com/questions/28337117/how-to-pivot-a-dataframe-in-pandas

An example "full study" (at small scale) is illustrated in ~/.antspyt1w/run_dlbs.py which demonstrates/comments on:

• how to aggregate dataframes
• how to pivot to wide format
• how to join with a demographic/metadata file
• visualizing basic outcomes.

ssl error

if you get an odd certificate error when calling force_download, try:

import ssl
ssl._create_default_https_context = ssl._create_unverified_context

to publish a release

before doing this - make sure you have a recent run of pip-compile pyproject.toml

rm -r -f build/ antspyt1w.egg-info/ dist/
python3 -m  build .
python3 -m pip install --upgrade twine
python3 -m twine upload --repository antspyt1w dist/*