def read_imgs(file, n):
imgs=[]
with open(file) as f:
content = f.read()
for line, row in enumerate(content.split()):
temp = [element for element in row.split(',') if element] # handling w/space
if len(temp) != n:
raise FileNotFoundError(f'Columns don\'t have same number of entries: check line {line} in {file}')
for img in temp:
if not isfile(img):
raise FileNotFoundError(f'{img} does not exist: check line {line} in {file}')
imgs.append(temp)
return np.array(imgs)
def show_progress(verbose=False):
config = ConfigParser()
config.read(pjoin(FILEDIR, 'config.ini'))
outDir = config['DEFAULT']['outDir']
modalities = [x for x in config['DEFAULT']['modalities'].split(',')]
# read caselist
cases = read_cases(pjoin(outDir, 'log', 'caselist.txt'))
num_cases = len(cases)
# read start time
start_time_file = pjoin(outDir, 'log', 'start_time.txt')
start_time = read_time(start_time_file)
# read final time
final_time_file = pjoin(outDir, 'log', 'final_time.txt')
if isfile(final_time_file):
final_time = read_time(final_time_file)
else:
final_time = datetime.now()
print('Output directory: ', outDir)
print('Number of cases to process: ', num_cases)
for modality in modalities:
modality_progress(outDir, modality, num_cases, verbose)
# show duration
duration_in_seconds = (final_time - start_time).total_seconds()
days = divmod(duration_in_seconds, 86400)
hours = divmod(days[1], 3600) # Use remainder of days to calc hours
minutes = divmod(hours[1], 60) # Use remainder of hours to calc minutes
seconds = divmod(minutes[1], 1) # Use remainder of minutes to calc seconds
print(
"\nTime taken so far: %d days, %d hours, %d minutes and %d seconds\n" %
(days[0], hours[0], minutes[0], seconds[0]))
def process(args):
cases= read_cases(args.caselist)
cases.sort()
# organize images into different directories ===========================================================
# outDir
# |
# ------------------------------------------------------------------------------------------------------
# | | | | | | | |
# | | | | | | | |
# transform template FA MD AD RD log stats
# | (same inner file structure as that of FA)
# |
# ----------------------------------------
# | | | | |
# preproc origdata warped skeleton roi
#
# copy all FA into FA directory
# put all preprocessed data into preproc directory
# keep all warp/affine in transform directory
# output all warped images in warped directory
# output all skeletons in skel directory
# output ROI based analysis files in roi directory
# save all ROI statistics, mean, and combined images
# define directories
modDir = pjoin(args.outDir, f'{args.modality}')
# args.xfrmDir = pjoin(args.outDir, 'transform')
# args.statsDir = pjoin(args.outDir, 'stats')
templateDir = pjoin(args.outDir, 'template/') # trailing slash is important for antsMultivariate*.sh
preprocDir= pjoin(modDir, 'preproc')
warpDir= pjoin(modDir, 'warped')
skelDir= pjoin(modDir, 'skeleton')
roiDir= pjoin(modDir, 'roi')
# force creation of inner directories
makeDirectory(warpDir, True)
makeDirectory(skelDir, True)
makeDirectory(roiDir, True)
# modality can be one of [FA,MD,AD,RD]
# we could use just listdir(), but the following would be stricter and safer
# since cases are sorted and we named images as modDir/{c}.nii.gz
# the following sort puts modImgs in the same order as that of cases
modImgs = glob(pjoin(modDir, '*.nii.gz'))
modImgs.sort()
if not args.noFillHole:
print('\nFilling holes inside the brain region in diffusion measure images')
# fill holes in all modality images
# caveat: origdata no longer remain origdata, become hole filled origdata
pool= Pool(args.ncpu)
pool.map_async(fillHoles, modImgs, error_callback= RAISE)
pool.close()
pool.join()
# preprocessing ========================================================================================
if args.modality=='FA':
print('Preprocessing FA images: eroding them and zeroing the end slices ...')
modDir= pjoin(args.outDir, args.modality)
CURRDIR= getcwd()
chdir(modDir)
check_call('tbss_1_preproc *.nii.gz', shell= True) # creates 'FA' and 'origdata' folders
chdir(CURRDIR)
print('Index file location has changed, see ', pjoin(preprocDir, 'slicesdir', 'index.html'))
# rename args.modality/FA to args.modality/preproc
move(pjoin(modDir, 'FA'), preprocDir)
else:
print(f'Preprocessing {args.modality} images using FA mask (eroding them and zeroing the end slices) ...')
modDir = pjoin(args.outDir, args.modality)
# force creation of inner directories
makeDirectory(pjoin(modDir, 'origdata'), True)
makeDirectory(pjoin(modDir, 'preproc'), True)
pool= Pool(args.ncpu)
for c, imgPath in zip(cases, modImgs):
FAmask= pjoin(args.outDir, 'FA', 'preproc', f'{c}_FA_mask.nii.gz')
preprocMod= pjoin(preprocDir, f'{c}_{args.modality}.nii.gz')
pool.apply_async(_fslmask, (imgPath, FAmask, preprocMod), error_callback= RAISE)
pool.close()
pool.join()
check_call((' ').join(['mv', pjoin(modDir, '*.nii.gz'), pjoin(modDir, 'origdata')]), shell= True)
modImgs = glob(pjoin(preprocDir, f'*{args.modality}.nii.gz'))
modImgs.sort()
# create template ======================================================================================
if not args.template and args.modality=='FA':
print('Creating study specific template ...')
# we could pass modImgs directly to antsMult(), instead saving them to a .txt file for logging
# modImgs = glob(pjoin(preprocDir, f'*{args.modality}*.nii.gz'))
makeDirectory(templateDir, args.force)
antsMultCaselist = pjoin(args.logDir, 'antsMultCaselist.txt')
with open(antsMultCaselist, 'w') as f:
for imgPath in modImgs:
f.write(imgPath+'\n')
# ATTN: antsMultivariateTemplateConstruction2.sh requires '/' at the end of templateDir
antsMult(antsMultCaselist, templateDir, args.logDir, args.ncpu, args.verbose)
# TODO: rename the template
args.template= pjoin(templateDir, 'template0.nii.gz')
check_call(f'ln -s {args.template} {args.statsDir}', shell= True)
# warp and affine to template0.nii.gz have been created for each case during template construction
# so template directory should be the transform directory
args.xfrmDir= templateDir
# register each image to the template ==================================================================
elif args.template:
# find warp and affine of FA image to args.template for each case
if args.modality=='FA':
print(f'Registering FA images to {args.template} space ..')
makeDirectory(args.xfrmDir, True)
pool= Pool(args.ncpu)
for c, imgPath in zip(cases, modImgs):
pool.apply_async(antsReg, (args.template, imgPath, pjoin(args.xfrmDir, f'{c}_FA'), args.logDir, args.verbose),
error_callback= RAISE)
pool.close()
pool.join()
# register template to a standard space ================================================================
# useful when you would like to do ROI based analysis using an atlas
# project the created/specified template to the space of atlas
if args.space:
outPrefix = pjoin(args.xfrmDir, 'tmp2space')
warp2space = outPrefix + '1Warp.nii.gz'
trans2space = outPrefix + '0GenericAffine.mat'
if not isfile(warp2space):
print(f'Registering {args.template} to the space of {args.space} ...')
antsReg(args.space, args.template, outPrefix, args.logDir, args.verbose)
# TODO: rename the template
args.template = outPrefix + 'Warped.nii.gz'
if basename(args.template) not in listdir(args.statsDir):
check_call(f'ln -s {args.template} {args.statsDir}', shell= True)
pool= Pool(args.ncpu)
for c, imgPath in zip(cases, modImgs):
# generalize warp and affine
warp2tmp= glob(pjoin(args.xfrmDir, f'{c}_FA*1Warp.nii.gz'))[0]
trans2tmp= glob(pjoin(args.xfrmDir, f'{c}_FA*0GenericAffine.mat'))[0]
output= pjoin(warpDir, f'{c}_{args.modality}_to_target.nii.gz')
if not args.space:
# print(f'Warping {imgPath} to template space ...')
pool.apply_async(_antsApplyTransforms, (imgPath, output, args.template, warp2tmp, trans2tmp),
error_callback= RAISE)
else:
# print(f'Warping {imgPath} to template-->standard space ...')
pool.apply_async(_antsApplyTransforms, (imgPath, output, args.space, warp2tmp, trans2tmp, warp2space, trans2space),
error_callback= RAISE)
pool.close()
pool.join()
# create skeleton for each subject
modImgsInTarget= glob(pjoin(warpDir, f'*_{args.modality}_to_target.nii.gz'))
modImgsInTarget.sort()
miFile= None
if args.modality=='FA':
print(f'Logging MI between warped images {warpDir}/*.nii.gz and target {args.template} ...')
miFile= measureSimilarity(modImgsInTarget, cases, args.template, args.logDir, args.ncpu)
# obtain modified args from skeletonize() which will be used for other modalities than FA
args= skeletonize(modImgsInTarget, cases, args, skelDir, miFile)
skelImgsInSub= glob(pjoin(skelDir, f'*_{args.modality}_to_target_skel.nii.gz'))
skelImgsInSub.sort()
# roi based analysis
if args.labelMap:
roi_analysis(skelImgsInSub, cases, args, roiDir, args.ncpu)
return args
from tbssUtil import pjoin, RAISE, environ, isfile
from plumbum.cmd import antsRegistration, MeasureImageSimilarity, head, cut
from plumbum import FG
from multiprocessing import Pool
import numpy as np
# determine ANTS_VERSION
# $ antsRegistration --version
# ANTs Version: 2.2.0.dev233-g19285
# Compiled: Sep 2 2018 23:23:33
antsVerFile = '/tmp/ANTS_VERSION_' + environ['USER']
if not isfile(antsVerFile):
(antsRegistration['--version'] > antsVerFile) & FG
with open(antsVerFile) as f:
content = f.read().split('\n')
ANTS_VERSION = content[0].split()[-1]
def computeMI(target, img, miFile):
if ANTS_VERSION <= '2.1.0':
(MeasureImageSimilarity['3', '2', target, img] | head['-n', '-2']
| cut['-d ', '-f6'] > miFile)()
else:
(MeasureImageSimilarity['-d', '3', '-m',
'MI[{},{},1,256]'.format(target, img)] >
miFile) & FG