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convert_atlas_mask.py
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convert_atlas_mask.py
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import pandas as pd
import numpy as np
from dipy.io.image import load_nifti, save_nifti
from computer_nav import load_nifti_remote, make_temppath
def atlas_converter(ROI_excel):
df = pd.read_excel(ROI_excel, sheet_name='Sheet1')
df['Structure'] = df['Structure'].str.lower()
index1=df['index']
index2=df['index2']
index3=df['index3']
structures=df['Structure']
hemispheres=df['Hemisphere']
hemispheres_new = []
for i in np.arange(np.size(hemispheres)):
if hemispheres[i] == "Left":
hemispheres_new.append('_left')
if hemispheres[i] == "Right":
hemispheres_new.append('_right')
converter_lr = {}
converter_comb = {}
index_to_struct_lr = {}
index_to_struct_comb = {}
for i in np.arange(np.size(hemispheres_new)):
if hemispheres_new[i] in ['_left','_right']:
converter_lr[index2[i]] = index1[i]
converter_comb[index2[i]] = index3[i]
index_to_struct_lr[index1[i]] = structures[i] + hemispheres_new[i]
index_to_struct_comb[index3[i]] = structures[i]
if 0 not in converter_lr:
converter_lr[0] = 0
if 0 not in converter_comb:
converter_comb[0] = 0
return converter_lr, converter_comb, index_to_struct_lr, index_to_struct_comb
def IIT_converter(ROI_excel):
df = pd.read_excel(ROI_excel, sheet_name='Sheet1')
df['Structure'] = df['Structure'].str.lower()
index1=df['index']
index2=df['index2']
structures=df['Structure']
hemispheres=df['Hemisphere']
hemispheres_new = []
for i in np.arange(np.size(hemispheres)-1):
if hemispheres[i] == "Left":
hemispheres_new.append('_left')
if hemispheres[i] == "Right":
hemispheres_new.append('_right')
converter_lr = {}
converter_comb = {}
index_to_struct_lr = {}
index_to_struct_comb = {}
for i in np.arange(np.size(index1)-1):
converter_lr[index2[i]] = index1[i]
converter_comb[index2[i]] = index3[i]
index_to_struct_lr[index1[i]] = structures[i] + hemispheres_new[i]
index_to_struct_comb[index3[i]] = structures[i]
return converter_lr, converter_comb, index_to_struct_lr, index_to_struct_comb
def convert_labelmask(atlas, converter, atlas_outpath = None, affine_labels=None, sftp=None):
if isinstance(atlas, str):
labels, affine_labels, _, _, _ = load_nifti_remote(atlas, sftp)
else:
if affine_labels is None:
raise TypeError('Need to add the affine labels if directly including label array')
else:
labels = atlas
labels_new = np.copy(labels)
for i in range(np.shape(labels)[0]):
for j in range(np.shape(labels)[1]):
for k in range(np.shape(labels)[2]):
try:
labels_new[i, j, k] = converter[labels[i, j, k]]
except:
print('hi')
if sftp is not None:
save_nifti(make_temppath(atlas_outpath), labels_new, affine_labels)
sftp.put(make_temppath(atlas_outpath), atlas_outpath)
else:
save_nifti(atlas_outpath, labels_new, affine_labels)
return(labels_new)
def run_onall():
atlas_legends = "/Users/alex/jacques/connectomes_testing/atlases/CHASSSYMM3AtlasLegends.xlsx"
df = pd.read_excel(atlas_legends, sheet_name='Sheet1')
df['Structure'] = df['Structure'].str.lower()
index1=df['index']
index2=df['index2']
l = ['N57442', 'N57446', 'N57447','N57449','N57451','N57496','N57498','N57500','N57502','N57504','N57513','N57515','N57518', 'N57520','N57522','N57546','N57447','N57449','N57451','N57496','N57498','N57500','N57502','N57504','N57513','N57515','N57518','N57520','N57522','N57546','N57548', 'N57550', 'N57552', 'N57554', 'N57559', 'N57580', 'N57582', 'N57584', 'N57587', 'N57590', 'N57692', 'N57694', 'N57700', 'N57702', 'N57709']
converter_lr, converter_comb = chassym3_converter(atlas_legends)
atlas_folder = "/Volumes/Data/Badea/Lab/mouse/C57_JS/DWI_RAS_40subj/"
for subject in l:
labels, affine_labels = load_nifti(atlas_folder + subject + "_chass_symmetric3_labels_RAS.nii.gz")
rslt_whitem = df.loc[df['Subdivisions_7'] == "7_whitematter"]
labels_leftright = np.copy(labels)
labels_combinedlr = np.copy(labels)
# for i in range(int(np.shape(labels)[0]/2-2), int(np.shape(labels)[0])):
for i in range(np.shape(labels)[0]):
for j in range(np.shape(labels)[1]):
for k in range(np.shape(labels)[2]):
if labels[i, j, k] > 1000:
labels_leftright[i, j, k] = converter_lr[labels[i,j,k]]
labels_combinedlr[i, j, k] = converter_comb[labels[i,j,k]]
save_nifti(atlas_folder + subject + "_chass_symmetric3_labels_RAS_combined.nii.gz", labels_combinedlr,
affine_labels)
save_nifti(atlas_folder + subject + "_chass_symmetric3_labels_RAS_lrordered.nii.gz", labels_leftright,
affine_labels)
print("done")
"""
for subject in l:
labels, affine_labels = load_nifti(atlas_folder + subject + "_chass_symmetric3_labels_RAS.nii.gz")
rslt_whitem = df.loc[df['Subdivisions_7'] == "7_whitematter"]
labels_leftright = np.copy(labels)
labels_combinedlr = np.copy(labels)
#for i in range(int(np.shape(labels)[0]/2-2), int(np.shape(labels)[0])):
for i in range(np.shape(labels)[0]):
for j in range(np.shape(labels)[1]):
for k in range(np.shape(labels)[2]):
if labels[i,j,k]>1000:
labels_leftright[i, j, k] = labels[i, j, k] - 834
labels_combinedlr[i, j, k] = labels[i, j, k] - 1000
save_nifti(atlas_folder + subject + "_chass_symmetric3_labels_RAS_combined.nii.gz", labels_combinedlr, affine_labels)
save_nifti(atlas_folder + subject + "_chass_symmetric3_labels_RAS_lrordered.nii.gz", labels_leftright, affine_labels)
print("done")
print("hi")
"""