# curvanato
local curvature quantification for anatomical data
### **Installation & Testing**
To install the package locally, navigate to the package root and run:
```bash
pip install .
```
## Usage
```python
import ants
import antspynet
import antspyt1w
import curvanato
import re
import os # For checking file existence
import pandas as pd
import numpy as np
os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "32"
# ANTPD data from open neuro
fn='.//bids/sub-RC4111/ses-1/anat/sub-RC4111_ses-1_T1w.nii.gz' # easy
fn='.//bids/sub-RC4103/ses-1/anat/sub-RC4103_ses-1_T1w.nii.gz'
fn='./bids//sub-RC4110/ses-2/anat/sub-RC4110_ses-2_T1w.nii.gz'
if os.path.exists(fn):
t1=ants.image_read( fn )
t1=ants.resample_image( t1, [0.5, 0.5, 0.5], use_voxels=False, interp_type=0 )
hoafn = re.sub( ".nii.gz", "_hoa.nii.gz" , fn )
if not os.path.exists(hoafn):
hoa = antspynet.harvard_oxford_atlas_labeling(t1, verbose=True)['segmentation_image']
ants.image_write( hoa, hoafn)
hoa = ants.image_read( hoafn )
citfn = re.sub( ".nii.gz", "_cit168.nii.gz" , fn )
if not os.path.exists(citfn):
t1b = antspynet.brain_extraction( t1, modality="t1threetissue" )['segmentation_image'].threshold_image(1,1)
t1r = ants.rank_intensity( t1 * t1b )
cit = antspyt1w.deep_cit168( t1r, verbose=True)['segmentation']
ants.image_write( cit, citfn)
cit = ants.image_read( citfn )
###################################################################################
###################################################################################
###################################################################################
###################################################################################
ctype='cit'
tcaudL=curvanato.load_labeled_caudate( option='hmt', binarize=False, label=[1,3,5] )
tcaudR=curvanato.load_labeled_caudate( option='hmt', binarize=False, label=[2,4,6] )
vlab=None
leftside=True
gr=0
subd=0
otherside=True
if otherside:
ccfn = [
re.sub( ".nii.gz", "_"+ctype+"Rkappa.nii.gz" , fn ),
re.sub( ".nii.gz", "_"+ctype+"R.nii.gz" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Rthk.nii.gz" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Rkappa.csv" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Rkappa.png" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Rthk.png" , fn ) ]
pcaud=[3,4]
plabs=[4]
if ctype == 'cit':
mytl=18
xx = curvanato.t1w_caudcurv( cit, target_label=mytl, ventricle_label=vlab,
prior_labels=pcaud, prior_target_label=plabs, subdivide=subd, grid=gr,
priorparcellation=tcaudR, plot=True,
verbose=True )
ants.plot( xx[0], xx[1], crop=True, axis=2, nslices=21, ncol=7, filename=ccfn[4] )
ants.plot( xx[0], xx[2], crop=True, axis=2, nslices=21, ncol=7, filename=ccfn[5] )
for j in range(3):
ants.image_write( xx[j], ccfn[j] )
xx[3].to_csv( ccfn[3] )
if leftside:
mytl=2
ccfn = [
re.sub( ".nii.gz", "_"+ctype+"Lkappa.nii.gz" , fn ),
re.sub( ".nii.gz", "_"+ctype+"L.nii.gz" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Lthk.nii.gz" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Lkappa.csv" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Lkappa.png" , fn ),
re.sub( ".nii.gz", "_"+ctype+"Lthk.png" , fn ) ]
print("Begin " + fn + " caud kap")
pcaud=[1,2]
plabs=[2]
xx = curvanato.t1w_caudcurv( cit, target_label=2, ventricle_label=vlab,
prior_labels=pcaud, prior_target_label=plabs, subdivide=subd, grid=gr,
priorparcellation=tcaudL, plot=True, searchrange=20,
verbose=True )
ants.plot( xx[0], xx[1], crop=True, axis=2, nslices=21, ncol=7, filename=ccfn[4] )
ants.plot( xx[0], xx[1], crop=True, axis=2, nslices=21, ncol=7, filename=ccfn[5] )
for j in range(3):
ants.image_write( xx[j], ccfn[j] )
xx[3].to_csv( ccfn[3] )
```
## Example data
this package has been tested on [ANTPD data from openneuro](https://openneuro.org/datasets/ds001907/versions/2.0.3).
could also try data [here](https://openneuro.org/datasets/ds004560/versions/1.0.1) which included repeated T1w acquisitions on same subjects but with different parameters. however, last time i tried this, the link was not working.
```
rm -r -f build/ curvanato.egg-info/ dist/
python3 -m build .
python3 -m pip install --upgrade twine
python3 -m twine upload --repository antspyt1w dist/*
```
Raw data
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