def split_multiatlas(atlas, output_prefix, pool=None):
"""
Take a multi-atlas with many ROIs and split it into binary masks for each ROI.
output_prefix can be a container with integer index keys as in the atlas image
and values the full output path.
"""
N = int(
os.popen('MeasureMinMaxMean 3 %s' % atlas).read().split('[',
1)[-1].split(
']', 1)[0])
if pool is None:
pool = BetterPool()
cmds = []
for idx in xrange(N):
if isinstance(output_prefix, str):
output = '%s-%s.nii.gz' % (output_prefix, idx)
else:
try:
output = output_prefix[idx]
except (IndexError, KeyError):
print 'Skipping %d' % idx
continue
cmds.append('ThresholdImage 3 %s %s %d %d' % (atlas, output, idx, idx))
pool.map(command, cmds)
def __init__(self, labels, pool=None):
self.volume = {}
self.overlap = {}
if pool is None:
pool = BetterPool()
# TODO refactor using knowledge that it's a symmetric matrix of values
# Determine pairwise overlap
for label1 in labels:
overlap_params = []
for label2 in labels:
overlap_params.append((label1, label2))
datum = pool.map(overlap_c3d, overlap_params)
vol1, vol2, over = zip(*datum)
# This overwrites same value a lot, bit redundant
self.volume = dict(zip(labels, vol2))
self.overlap[label1] = dict(zip(labels, over))
# Approximately Thomas's method, can debate about order of some elements, like VLP and MTT/VA
# ['2-AV', '4-VA', '5-VLa', '6-VLP', '7-VPL', '8-Pul', '9-LGN', '10-MGN', '11-CM', '12-MD-Pf', '13-Hb', '14-MTT']
# methods = {
# 'Thomas': ['8-Pul', '11-CM', '12-MD-Pf', '4-VA', '14-MTT', '2-AV', '13-Hb', '6-VLP', '5-VLa', '7-VPL', '9-LGN', '10-MGN'],
# 'Fixed_Metric': ['2-AV', '6-VLP', '4-VA', '5-VLa', '8-Pul', '7-VPL', '9-LGN', '10-MGN', '12-MD-Pf', '11-CM', '13-Hb', '14-MTT'],
# }
find_num = re.compile('[0-9]+')
if __name__ == '__main__':
if len(sys.argv) < 3:
print '%s <method: Numerical, Metric> <output_atlas> labels ...' % sys.argv[
0]
sys.exit(0)
pool = BetterPool()
method = sys.argv[1]
out = sys.argv[2]
labels = sys.argv[3:]
if method == 'Numerical':
print 'Using the input order'
method_labels = labels
elif method == 'Metric':
# Use overlap metric based comparison
print 'Calculating overlap metric weighted by volume'
compare = CompareOverlap(labels, pool)
method_labels = sorted(labels, cmp=compare)
label_numbers = dict()
for i, label in enumerate(labels):
label_num = find_num.search(os.path.basename(label))
if label_num is None:
'-p',
'--processes',
nargs='?',
default=None,
const=None,
type=int,
help=
'number of parallel processes to use. If unspecified, automatically set to number of CPUs.'
)
# parser.add_argument('-v', '--verbose', action='store_true', help='verbose mode')
# parser.add_argument('-d', '--debug', action='store_true', help='debug mode, interactive prompts')
# parser.add_argument('-R', '--right', action='store_true', help='segment right thalamus')
if __name__ == '__main__':
args = parser.parse_args()
pool = BetterPool(args.processes)
method = args.method
out = args.output_path
labels = args.labels
if method == 'Numerical':
print 'Using the input order'
method_labels = labels
elif method == 'Metric':
# Use overlap metric based comparison
print 'Calculating overlap metric weighted by volume'
compare = CompareOverlap(labels, pool)
method_labels = sorted(labels, cmp=compare)
label_numbers = dict()
for i, label in enumerate(labels):
label_num = find_num.search(os.path.basename(label))
if label_num is None: