def modality_progress(outDir, modality, num_cases, verbose):
modDir = pjoin(outDir, modality)
origDir = pjoin(modDir, 'origdata')
preprocDir = pjoin(modDir, 'preproc')
warpDir = pjoin(modDir, 'warped')
skelDir = pjoin(modDir, 'skeleton')
roiDir = pjoin(modDir, 'roi')
# organize progress in a dataframe according to caseid
print(f'\nProgress of {modality} TBSS:\n')
df = pd.DataFrame(
columns=['origdata', 'preprocessed', 'warped', 'skeletonized', 'roi'])
# origdata
List_actual, List = glob_dir(num_cases, pjoin(origDir, '*.nii.gz'))
print('origdata obtained: ', len(List_actual))
df['origdata'] = [basename(imgPath).split('.')[0] for imgPath in List]
# preproc
List_actual, List = glob_dir(num_cases,
pjoin(preprocDir, f'*{modality}.nii.gz'))
print('pre-processed: ', len(List_actual))
df['preprocessed'] = [basename(imgPath).split('.')[0] for imgPath in List]
# warped
List_actual, List = glob_dir(
num_cases, pjoin(warpDir, f'*{modality}_to_target.nii.gz'))
print('registered to template space: ', len(List_actual))
df['warped'] = [basename(imgPath).split('.')[0] for imgPath in List]
# skeleton
List_actual, List = glob_dir(
num_cases, pjoin(skelDir, f'*{modality}_to_target_skel.nii.gz'))
print('skeletonized: ', len(List_actual))
df['skeletonized'] = [basename(imgPath).split('.')[0] for imgPath in List]
# roi
List_actual, List = glob_dir(num_cases,
(pjoin(roiDir, f'*{modality}_roi.csv')))
print('roi-based stat calculated: ', len(List_actual))
df['roi'] = [basename(imgPath).split('.')[0] for imgPath in List]
if verbose:
with pd.option_context('display.max_rows', None, 'display.max_columns',
None):
print(
'\nNOTE: Enlarge your terminal to have a better view of the dashboard\n'
)
print(df)
def checkDuplicity(imgs, cases):
# for c1 in cases:
# count=0
# for c2 in cases:
# if c1==c2:
# count+=1
# if count>1:
# warn(f'Caseid {c1} is not unique, '
# 'it exists multiple times or occurs as a substring in multiple caseids')
print('\nChecking for duplicity of caseids in input images')
for c in cases:
dupPath=[]
for imgPath in imgs:
if c in imgPath:
dupPath.append(basename(imgPath))
if len(dupPath)>1:
print(f'One (or some) of the caseids don\'t uniquely represent input images. '
f'For example, caseid {c} exists as a substring in multiple images: ', dupPath)
raise ValueError('Either remove conflicting imgs/cases or provide --input IMAGELIST.csv')
def write_caselist(logDir, List=None, Dir=None):
if Dir is not None:
imgs= glob(pjoin(Dir, '*.nii.gz'))
elif List is not None:
try:
# if List.shape[1]>1:
imgs= List[ :,0]
except:
imgs= List
caselist=pjoin(logDir,'caselist.txt')
cases=[]
with open(caselist, 'w') as f:
for img in imgs:
caseid= basename(img).split('.')[0]
cases.append(caseid)
f.write(caseid+'\n')
return (caselist,cases)
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