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
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brain extraction
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denoising
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n4 bias correction
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brain parcellation into tissues, hemispheres, lobes and regions
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hippocampus specific segmentation
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t1 hypointensity segmentation and classification exploratory
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deformable registration with robust and repeatable parameters
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registration-based labeling of major white matter tracts
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helpers that organize and annotate segmentation variables into data frames
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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/*
1 maintainer
Metadata
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Release files for antspyt1w 1.1.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| antspyt1w-1.1.3.tar.gz | 48.5 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| antspyt1w-1.1.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 89.8 kB
Release files / antspyt1w-1.1.3.tar.gz
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