def __init__(self, args, mode, dataset_path='../datasets', classes=4, dim=(32, 32, 32), split_id=0, samples=1000,
load=False):
self.mode = mode
self.root = dataset_path
self.classes = classes
dataset_name = "mrbrains" + str(classes)
self.training_path = self.root + '/mrbrains_2018/training'
self.dirs = os.listdir(self.training_path)
self.samples = samples
self.list = []
self.full_vol_size = (240, 240, 48)
self.threshold = 0.1
self.crop_dim = dim
self.list_flair = []
self.list_ir = []
self.list_reg_ir = []
self.list_reg_t1 = []
self.labels = []
self.full_volume = None
self.save_name = self.root + '/mrbrains_2018/training/mrbrains_2018-classes-' + str(
classes) + '-list-' + mode + '-samples-' + str(
samples) + '.txt'
if load:
## load pre-generated data
self.list = utils.load_list(self.save_name)
return
subvol = '_vol_' + str(dim[0]) + 'x' + str(dim[1]) + 'x' + str(dim[2])
self.sub_vol_path = self.root + '/mrbrains_2018/generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
list_reg_t1 = sorted(glob.glob(os.path.join(self.training_path, '*/pr*/*g_T1.nii.gz')))
list_reg_ir = sorted(glob.glob(os.path.join(self.training_path, '*/pr*/*g_IR.nii.gz')))
list_flair = sorted(glob.glob(os.path.join(self.training_path, '*/pr*/*AIR.nii.gz')))
labels = sorted(glob.glob(os.path.join(self.training_path, '*/*egm.nii.gz')))
self.affine = img_loader.load_affine_matrix(list_reg_t1[0])
split_id = int(split_id)
if mode == 'val':
labels = [labels[split_id]]
list_reg_t1 = [list_reg_t1[split_id]]
list_reg_ir = [list_reg_ir[split_id]]
list_flair = [list_flair[split_id]]
self.full_volume = get_viz_set(list_reg_t1, list_reg_ir, list_flair, labels, dataset_name=dataset_name)
else:
labels.pop(split_id)
list_reg_t1.pop(split_id)
list_reg_ir.pop(split_id)
list_flair.pop(split_id)
self.list = create_sub_volumes(list_reg_t1, list_reg_ir, list_flair, labels,
dataset_name=dataset_name, mode=mode,
samples=samples, full_vol_dim=self.full_vol_size,
crop_size=self.crop_dim, sub_vol_path=self.sub_vol_path,
th_percent=self.threshold)
utils.save_list(self.save_name, self.list)
def __init__(self,
mode,
dataset_path='./datasets',
crop_dim=(32, 32, 32),
split_id=1,
samples=1000,
load=False):
"""
:param mode: 'train','val','test'
:param dataset_path: root dataset folder
:param crop_dim: subvolume tuple
:param fold_id: 1 to 10 values
:param samples: number of sub-volumes that you want to create
"""
self.mode = mode
self.root = str(dataset_path)
self.training_path = self.root + '/iseg_2017/iSeg-2017-Training/'
self.testing_path = self.root + '/iseg_2017/iSeg-2017-Testing/'
self.CLASSES = 4
self.full_vol_dim = (144, 192, 256) # slice, width, height
self.crop_size = crop_dim
self.list = []
self.samples = samples
self.full_volume = None
self.save_name = self.root + '/iseg_2017/iSeg-2017-Training/iseg2017-list-' + mode + '-samples-' + str(
samples) + '.txt'
if load:
self.list = utils.load_list(self.save_name)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, '*T1.img')))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
return
subvol = '_vol_' + str(crop_dim[0]) + 'x' + str(
crop_dim[1]) + 'x' + str(crop_dim[2])
self.sub_vol_path = self.root + '/iseg_2017/generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, '*T1.img')))
list_IDsT2 = sorted(
glob.glob(os.path.join(self.training_path, '*T2.img')))
labels = sorted(
glob.glob(os.path.join(self.training_path, '*label.img')))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
if self.mode == 'train':
list_IDsT1 = list_IDsT1[:split_id]
list_IDsT2 = list_IDsT2[:split_id]
labels = labels[:split_id]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT2,
labels,
dataset_name="iseg2017",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
threshold=10)
elif self.mode == 'val':
list_IDsT1 = list_IDsT1[split_id:]
list_IDsT2 = list_IDsT2[:split_id:]
labels = labels[split_id:]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT2,
labels,
dataset_name="iseg2017",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
threshold=10)
self.full_volume = get_viz_set(list_IDsT1, list_IDsT2, labels)
elif self.mode == 'test':
self.list_IDsT1 = sorted(
glob.glob(os.path.join(self.testing_path, '*T1.img')))
self.list_IDsT2 = sorted(
glob.glob(os.path.join(self.testing_path, '*T2.img')))
self.labels = None
utils.save_list(self.save_name, self.list)
def __init__(self,
args,
mode,
dataset_path='./datasets',
crop_dim=(32, 32, 32),
split_id=1,
samples=1000,
load=False):
"""
:param mode: 'train','val','test'
:param dataset_path: root dataset folder
:param crop_dim: subvolume tuple
:param fold_id: 1 to 10 values
:param samples: number of sub-volumes that you want to create
"""
self.mode = mode
self.root = str(dataset_path)
self.training_path = self.root + '/iseg_2019/iSeg-2019-Training/'
self.testing_path = self.root + '/iseg_2019/iSeg-2019-Validation/'
self.CLASSES = 4
self.full_vol_dim = (144, 192, 256) # slice, width, height
self.crop_size = crop_dim
self.threshold = args.threshold
self.normalization = args.normalization
self.augmentation = args.augmentation
self.list = []
self.samples = samples
self.full_volume = None
self.save_name = self.root + '/iseg_2019/iseg2019-list-' + mode + '-samples-' + str(
samples) + '.txt'
if self.augmentation:
self.transform = augment3D.RandomChoice(transforms=[
augment3D.GaussianNoise(mean=0, std=0.01),
augment3D.RandomFlip(),
augment3D.ElasticTransform()
],
p=0.5)
if load:
## load pre-generated data
self.list = utils.load_list(self.save_name)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, '*T1.img')))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
return
subvol = '_vol_' + str(crop_dim[0]) + 'x' + str(
crop_dim[1]) + 'x' + str(crop_dim[2])
self.sub_vol_path = self.root + '/iseg_2019/generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, '*T1.img')))
list_IDsT2 = sorted(
glob.glob(os.path.join(self.training_path, '*T2.img')))
labels = sorted(
glob.glob(os.path.join(self.training_path, '*label.img')))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
if self.mode == 'train':
list_IDsT1 = list_IDsT1[:split_id]
list_IDsT2 = list_IDsT2[:split_id]
labels = labels[:split_id]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT2,
labels,
dataset_name="iseg2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
th_percent=self.threshold)
elif self.mode == 'val':
list_IDsT1 = list_IDsT1[split_id:]
list_IDsT2 = list_IDsT2[:split_id:]
labels = labels[split_id:]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT2,
labels,
dataset_name="iseg2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
th_percent=self.threshold)
self.full_volume = get_viz_set(list_IDsT1,
list_IDsT2,
labels,
dataset_name="iseg2019")
elif self.mode == 'test':
self.list_IDsT1 = sorted(
glob.glob(os.path.join(self.testing_path, '*T1.img')))
self.list_IDsT2 = sorted(
glob.glob(os.path.join(self.testing_path, '*T2.img')))
self.labels = None
# todo inference here
utils.save_list(self.save_name, self.list)