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nic_train_network_leave_one_out.py
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nic_train_network_leave_one_out.py
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# -----------------------------------------------------------------------------
# MS lesion segmentation pipeline
# ---------------------------------
# - incorporates:
# - MRI identification
# - registration
# - skull stripping
# - MS lesion segmentation training and testing using the CNN aproach
# of Valverde et al (NI2017)
#
# Sergi Valverde 2017
# svalverde@eia.udg.edu
# -----------------------------------------------------------------------------
import os
import sys
import platform
import time
import argparse
import ConfigParser
from utils.preprocess import preprocess_scan
from utils.load_options import load_options, print_options
os.system('cls' if platform.system() == 'Windows' else 'clear')
print "##################################################"
print "# ------------ #"
print "# nicMSlesions #"
print "# ------------ #"
print "# MS WM lesion segmentation #"
print "# #"
print "# ------------------------------- #"
print "# (c) Sergi Valverde 2018 #"
print "# Neuroimage Computing Group #"
print "# ------------------------------- #"
print "##################################################\n"
# load options from input
parser = argparse.ArgumentParser()
parser.add_argument('--docker',
dest='docker',
action='store_true')
parser.set_defaults(docker=False)
args = parser.parse_args()
container = args.docker
# link related libraries
CURRENT_PATH = os.getcwd()
sys.path.append(os.path.join(CURRENT_PATH, 'libs'))
# load default options and update them with user information
default_config = ConfigParser.SafeConfigParser()
default_config.read(os.path.join(CURRENT_PATH, 'config', 'default.cfg'))
user_config = ConfigParser.RawConfigParser()
user_config.read(os.path.join(CURRENT_PATH, 'config', 'configuration.cfg'))
# read user's configuration file
options = load_options(default_config, user_config)
options['tmp_folder'] = CURRENT_PATH + '/tmp'
if options['debug']:
print_options(options)
# tensorflow backend
device = str(options['gpu_number'])
print "DEBUG: ", device
os.environ['KERAS_BACKEND'] = 'tensorflow'
os.environ["CUDA_VISIBLE_DEVICES"] = device
# set paths taking into account the host OS
host_os = platform.system()
if host_os == 'Linux':
options['niftyreg_path'] = CURRENT_PATH + '/libs/linux/niftyreg'
options['robex_path'] = CURRENT_PATH + '/libs/linux/ROBEX/runROBEX.sh'
options['test_slices'] = 256
elif host_os == 'Windows':
options['niftyreg_path'] = os.path.normpath(
os.path.join(CURRENT_PATH, 'libs', 'win', 'niftyreg'))
options['robex_path'] = os.path.normpath(
os.path.join(CURRENT_PATH, 'libs', 'win', 'ROBEX', 'runROBEX.bat'))
options['test_slices'] = 256
else:
print "The OS system", host_os, "is not currently supported."
exit()
from CNN.base import train_cascaded_model, test_cascaded_model
from CNN.build_model import cascade_model
if container:
options['train_folder'] = os.path.normpath(
'/data' + options['train_folder'])
else:
options['train_folder'] = os.path.normpath(options['train_folder'])
scan_list = os.listdir(options['train_folder'])
scan_list.sort()
# --------------------------------------------------
# process all scans before leave-one-out-training
# move things to a tmp folder before starting
# --------------------------------------------------
for scan in scan_list:
total_time = time.time()
preprocess_time = time.time()
options['tmp_scan'] = scan
current_folder = os.path.join(options['train_folder'], scan)
options['tmp_folder'] = os.path.normpath(
os.path.join(current_folder, 'tmp'))
# set task to train
options['task'] = 'training'
# preprocess scan
preprocess_scan(current_folder, options)
# --------------------------------------------------
# WM MS lesion training
# - configure net and train leave one out
# --------------------------------------------------
for scan in scan_list:
seg_time = time.time()
print "> CNN: Starting training session for scan", scan
# select training scans
train_x_data = {f: {m: os.path.join(options['train_folder'], f, 'tmp', n)
for m, n in zip(options['modalities'],
options['x_names'])}
for f in scan_list if f != scan}
train_y_data = {f: os.path.join(options['train_folder'], f, 'tmp',
'lesion.nii.gz')
for f in scan_list if f != scan}
# organize the experiment: save models and traiining images inside a
# predifined folder. Network parameters and weights are stored inside
# test_folder/experiment/nets/
# training images are stored inside test_folder/experiment/.train
# final segmentation images are stored in test_folder/experiment
if not os.path.exists(os.path.join(options['train_folder'],
scan,
'nets')):
os.mkdir(os.path.join(options['train_folder'], scan, 'nets'))
options['weight_paths'] = os.path.join(options['train_folder'],
scan,
'nets')
options['load_weights'] = False
options['test_scan'] = scan
# train the model for the current scan
print "> CNN: training net with %d subjects" % (len(train_x_data.keys()))
# --------------------------------------------------
# initialize the CNN and train the classifier
# --------------------------------------------------
model = cascade_model(options)
model = train_cascaded_model(model, train_x_data, train_y_data, options)
print "> INFO: training time:", round(time.time() - seg_time), "sec"
print "> INFO: total pipeline time: ", \
round(time.time() - total_time), "sec"
# --------------------------------------------------
# test the current scan
# --------------------------------------------------
test_x_data = {scan: {m: os.path.join(options['train_folder'],
scan,
'tmp',
n)
for m, n in zip(options['modalities'],
options['x_names'])}}
out_seg = test_cascaded_model(model, test_x_data, options)
print "> INFO: All processes have been finished. Have a good day!"