def main():
subject = ipmi.get_unsegmented_subjects()[0]
ref_path = subject.t1_path
flo_path = Path('template_on_00_affine.nii.gz')
if not flo_path.is_file():
template_path = template.get_final_template().template_image_path
trsf_path = subject.template_to_t1_affine_path
reg.resample(template_path, ref_path, trsf_path, flo_path)
bending_energies = np.logspace(-3, -1, 10)
for be in bending_energies:
# Register
command = (f'reg_f3d -ref {ref_path} -flo {flo_path} '
f'-cpp cpp_be_{be:.5f}.nii.gz '
f'-res res_be_{be:.5f}.nii.gz -be {be}'.split())
call(command)
# Jacobian
command = (
f'reg_jacobian -ref {ref_path} '
f'-trans cpp_be_{be:.5f}.nii.gz -jac jac_be_{be:.5f}.nii.gz '
f'-jacL jacL_be_{be:.5f}.nii.gz'.split())
call(command)
# Jacobian label maps for 3D Slicer
make_label_map(f'jacL_be_{be:.5f}.nii.gz', 0,
f'jacL_be_{be:.5f}_label_map.nii.gz')
make_label_map(f'jac_be_{be:.5f}.nii.gz', 1,
f'jac_be_{be:.5f}_label_map.nii.gz')
reg.spline_to_displacement_field(f'{ref_path}',
f'cpp_be_{be:.5f}.nii.gz',
f'cpp_be_{be:.5f}_disp.nii.gz')
import csv
import numpy as np
from scipy.stats import pearsonr
import ipmi
from ipmi.constants import GREY_MATTER, WHITE_MATTER, BRAIN
def compute_and_write_row(writer, x, y, name):
cc, p = pearsonr(x, y)
writer.writerow(
{'Measurement': name, 'Pearson CC': f'{cc:.2f}', 'p': f'{p:.3f}'})
if __name__ == '__main__':
subjects = ipmi.get_unsegmented_subjects()
ages = [subject.age for subject in subjects]
volumes_tuples = [subject.get_volumes_normalised() for subject in subjects]
gms = np.array([tup.volumes[GREY_MATTER] for tup in volumes_tuples])
wms = np.array([tup.volumes[WHITE_MATTER] for tup in volumes_tuples])
brains = np.array([tup.volumes[BRAIN] for tup in volumes_tuples])
gms_norm = [tup.normalised_volumes[GREY_MATTER] for tup in volumes_tuples]
wms_norm = [tup.normalised_volumes[WHITE_MATTER] for tup in volumes_tuples]
brains_norm = [tup.normalised_volumes[BRAIN] for tup in volumes_tuples]
csv_path = ipmi.path.correlations_report_path
with open(str(csv_path), 'w') as csvfile:
fieldnames = 'Measurement', 'Pearson CC', 'p'
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
def __init__(self):
self.subjects = ipmi.get_segmented_subjects()
self.subjects.extend(ipmi.get_unsegmented_subjects())