def __init__(self,
mode,
sub_task='lung',
split=0.2,
fold=0,
n_classes=2,
samples=10,
dataset_path='../datasets',
crop_dim=(32, 32, 32)):
print("MANDIBLE SEGMENTATION DATASET")
self.CLASSES = n_classes
self.fold = int(fold)
self.crop_size = crop_dim
self.full_vol_dim = (256, 256, 192) # width, height,slice
self.mode = mode
self.full_volume = None
self.affine = None
self.list = []
self.samples = samples
subvol = '_vol_' + str(crop_dim[0]) + 'x' + str(
crop_dim[1]) + 'x' + str(crop_dim[2])
self.sub_vol_path = dataset_path + '/MRBrainS18/subvol_generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
self.train_images, self.train_labels, self.val_labels, self.val_images = [], [], [], []
list_images = sorted(
glob.glob(os.path.join(dataset_path, 'MRBrainS18/images/*')))
list_labels = sorted(
glob.glob(os.path.join(dataset_path, 'MRBrainS18/labels/*')))
len_of_data = len(list_images)
for i in range(len_of_data):
if i >= (self.fold * int(split * len_of_data)) and i < (
(self.fold * int(split * len_of_data)) +
int(split * len_of_data)):
self.train_images.append(list_images[i])
self.train_labels.append(list_labels[i])
else:
self.val_images.append(list_images[i])
self.val_labels.append(list_labels[i])
if (mode == 'train'):
self.list_IDs = self.train_images
self.list_labels = self.train_labels
elif (mode == 'val'):
self.list_IDs = self.val_images
self.list_labels = self.val_labels
self.list = create_sub_volumes(self.list_IDs,
self.list_labels,
dataset_name='covid19seg',
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path)
print("{} SAMPLES = {}".format(mode, len(self.list)))
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 = 'MICCAI_2020_ribfrac'
self.val_path = "/data/chelx/MICCAI-RibFrac2020/dataset/ribfrac-val-images"
self.dirs = os.listdir(self.val_path)
self.sample = samples
self.list = []
self.full_vol_size = (240, 240, 48)
self.threshold = args.threshold
self.crop_dim = dim
self.list_reg_t1 = []
self.labels = []
self.full_volume = None
self.save_name = self.root + '/MICCAI_2020_ribfrac/val/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]) + '_' + str(self.threshold)
# subvol = '_vol_' + str(dim[0]) + 'x' + str(dim[1]) + 'x' + str(dim[2]) + '_' + str(self.threshold) + '_' + str(args.samples_train)
subvol = '_vol_' + str(dim[0]) + 'x' + str(dim[1]) + 'x' + str(
dim[2]) + '_' + str(self.threshold) + '_overall'
self.sub_vol_path = self.root + '/MICCAI_2020_ribfrac/generated/' + mode + subvol + '/'
# utils.make_dirs(self.sub_vol_path)
if not os.path.exists(self.sub_vol_path):
os.makedirs(self.sub_vol_path)
if not os.path.exists(os.path.dirname(self.save_name)):
os.makedirs(os.path.dirname(self.save_name))
list_reg_t1 = sorted(
glob.glob(os.path.join(self.val_path, 'val/*.nii.gz')))
# import ipdb;ipdb.set_trace()
labels = sorted(
glob.glob(os.path.join(self.val_path, 'val_label_gt/*.nii.gz')))
self.affine = img_loader.load_affine_matrix(list_reg_t1[0])
self.list = create_sub_volumes(list_reg_t1,
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, 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',
classes=5,
crop_dim=(32, 32, 32),
split_idx=10,
samples=10,
load=False):
"""
:param mode: 'train','val','test'
:param dataset_path: root dataset folder
:param crop_dim: subvolume tuple
:param split_idx: 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 + '/MICCAI_BraTS_2018_Data_Training/'
self.testing_path = self.root + ' '
self.CLASSES = 4
self.full_vol_dim = (240, 240, 155) # 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.classes = classes
self.save_name = self.root + '/MICCAI_BraTS_2018_Data_Training/brats2018-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
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path,
'*GG/*/*t1.nii.gz')))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
self.list = utils.load_list(self.save_name)
return
subvol = '_vol_' + str(crop_dim[0]) + 'x' + str(
crop_dim[1]) + 'x' + str(crop_dim[2])
self.sub_vol_path = self.root + '/MICCAI_BraTS_2018_Data_Training/generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t1.nii.gz')))
list_IDsT1ce = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t1ce.nii.gz')))
list_IDsT2 = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t2.nii.gz')))
list_IDsFlair = sorted(
glob.glob(os.path.join(self.training_path,
'*GG/*/*_flair.nii.gz')))
labels = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*_seg.nii.gz')))
# print(len(list_IDsT1),len(list_IDsT2),len(list_IDsFlair),len(labels))
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
if self.mode == 'train':
list_IDsT1 = list_IDsT1[:split_idx]
list_IDsT1ce = list_IDsT1ce[:split_idx]
list_IDsT2 = list_IDsT2[:split_idx]
list_IDsFlair = list_IDsFlair[:split_idx]
labels = labels[:split_idx]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2018",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
normalization=self.normalization,
th_percent=self.threshold)
elif self.mode == 'val':
list_IDsT1 = list_IDsT1[split_idx:]
list_IDsT1ce = list_IDsT1ce[split_idx:]
list_IDsT2 = list_IDsT2[split_idx:]
list_IDsFlair = list_IDsFlair[split_idx:]
labels = labels[split_idx:]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2018",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path,
normalization=self.normalization,
th_percent=self.threshold)
elif self.mode == 'test':
self.list_IDsT1 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz')))
self.list_IDsT1ce = sorted(
glob.glob(os.path.join(self.testing_path,
'*GG/*/*t1ce.nii.gz')))
self.list_IDsT2 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz')))
self.list_IDsFlair = sorted(
glob.glob(
os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz')))
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)
def __init__(self,
args,
mode,
dataset_path='../../datasets',
classes=4,
crop_dim=(128, 128, 128),
split_idx=204,
samples=10,
load=False):
"""
:param mode: 'train','val','test'
:param dataset_path: root dataset folder
:param crop_dim: subvolume tuple
:param split_idx: 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 + '/brats2019/MICCAI_BraTS_2019_Data_Training/train_generated'
self.testing_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Validation/'
self.full_vol_dim = (128, 128, 128) # 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.classes = classes
if self.augmentation:
self.transform = augment3D.RandomChoice(transforms=[
augment3D.GaussianNoise(mean=0, std=0.01),
augment3D.RandomFlip(),
augment3D.ElasticTransform()
],
p=0.5)
self.save_name = self.root + '/brats2019/brats2019-list-' + mode + '-samples-' + str(
samples) + '.txt'
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,
'***_imgs/***_imgs_t1.npy')))
# 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 + '/brats2019/MICCAI_BraTS_2019_Data_Training/generated/' + mode + subvol + '/'
# if not os.path.exists(self.sub_vol_path):
# utils.make_dirs(self.sub_vol_path)
list_IDsT1 = sorted(
glob.glob(
os.path.join(self.training_path, '***_imgs/***_imgs_t1.npy')))
list_IDsT1ce = sorted(
glob.glob(
os.path.join(self.training_path,
'***_imgs/***_imgs_t1gd.npy')))
list_IDsT2 = sorted(
glob.glob(
os.path.join(self.training_path, '***_imgs/***_imgs_t2.npy')))
list_IDsFlair = sorted(
glob.glob(
os.path.join(self.training_path,
'***_imgs/***_imgs_flair.npy')))
labels = sorted(
glob.glob(
os.path.join(self.training_path, '***_seg/***_seg_label.npy')))
# list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels = utils.shuffle_lists(list_IDsT1, list_IDsT1ce,
# list_IDsT2,
# list_IDsFlair, labels,
# seed=17)
self.affine = np.diag([1, 1, 1, 1])
if self.mode == 'train':
list_IDsT1 = list_IDsT1[:split_idx]
list_IDsT1ce = list_IDsT1ce[:split_idx]
list_IDsT2 = list_IDsT2[:split_idx]
list_IDsFlair = list_IDsFlair[:split_idx]
labels = labels[:split_idx]
# self.list = create_sub_volumes(list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels,
# dataset_name="brats2019", 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)
# with open(self.save_name, "wb") as fp:
# pickle.dump(self.list,fp)
with open(self.save_name, "rb") as fp:
self.list = pickle.load(fp)
elif self.mode == 'val':
list_IDsT1 = list_IDsT1[split_idx:]
list_IDsT1ce = list_IDsT1ce[split_idx:]
list_IDsT2 = list_IDsT2[split_idx:]
list_IDsFlair = list_IDsFlair[split_idx:]
labels = labels[split_idx:]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2019",
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)
with open(self.save_name, "wb") as fp:
pickle.dump(self.list, fp)
# with open(self.save_name, "rb") as fp:
# self.list = pickle.load(fp)
elif self.mode == 'test':
self.list_IDsT1 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz')))
self.list_IDsT1ce = sorted(
glob.glob(os.path.join(self.testing_path,
'*GG/*/*t1ce.nii.gz')))
self.list_IDsT2 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz')))
self.list_IDsFlair = sorted(
glob.glob(
os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz')))
self.labels = None
def __init__(self,
mode,
dataset_path='./datasets',
classes=5,
crop_dim=(200, 200, 150),
split_idx=260,
samples=10,
load=False):
"""
:param mode: 'train','val','test'
:param dataset_path: root dataset folder
:param crop_dim: subvolume tuple
:param split_idx: 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 + '/brats2019/MICCAI_BraTS_2019_Data_Training/'
self.testing_path = self.root + '/brats2019/MICCAI_BraTS_2019_Data_Validation/'
self.full_vol_dim = (240, 240, 155) # slice, width, height
self.crop_size = crop_dim
self.list = []
self.samples = samples
self.full_volume = None
self.classes = classes
self.save_name = self.root + '/brats2019/brats2019-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,
'*GG/*/*t1.nii.gz')))
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 + '/brats2019/MICCAI_BraTS_2019_Data_Training/generated/' + mode + subvol + '/'
utils.make_dirs(self.sub_vol_path)
list_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t1.nii.gz')))
list_IDsT1ce = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t1ce.nii.gz')))
list_IDsT2 = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*t2.nii.gz')))
list_IDsFlair = sorted(
glob.glob(os.path.join(self.training_path,
'*GG/*/*_flair.nii.gz')))
labels = sorted(
glob.glob(os.path.join(self.training_path, '*GG/*/*_seg.nii.gz')))
list_IDsT1, list_IDsT1ce, list_IDsT2, list_IDsFlair, labels = utils.shuffle_lists(
list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
seed=17)
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
if self.mode == 'train':
print('Brats2019, Total data:', len(list_IDsT1))
list_IDsT1 = list_IDsT1[:split_idx]
list_IDsT1ce = list_IDsT1ce[:split_idx]
list_IDsT2 = list_IDsT2[:split_idx]
list_IDsFlair = list_IDsFlair[:split_idx]
labels = labels[:split_idx]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path)
elif self.mode == 'val':
list_IDsT1 = list_IDsT1[split_idx:]
list_IDsT1ce = list_IDsT1ce[split_idx:]
list_IDsT2 = list_IDsT2[split_idx:]
list_IDsFlair = list_IDsFlair[split_idx:]
labels = labels[split_idx:]
self.list = create_sub_volumes(list_IDsT1,
list_IDsT1ce,
list_IDsT2,
list_IDsFlair,
labels,
dataset_name="brats2019",
mode=mode,
samples=samples,
full_vol_dim=self.full_vol_dim,
crop_size=self.crop_size,
sub_vol_path=self.sub_vol_path)
elif self.mode == 'test':
self.list_IDsT1 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t1.nii.gz')))
self.list_IDsT1ce = sorted(
glob.glob(os.path.join(self.testing_path,
'*GG/*/*t1ce.nii.gz')))
self.list_IDsT2 = sorted(
glob.glob(os.path.join(self.testing_path, '*GG/*/*t2.nii.gz')))
self.list_IDsFlair = sorted(
glob.glob(
os.path.join(self.testing_path, '*GG/*/*_flair.nii.gz')))
self.labels = None
# Todo inference code here
utils.save_list(self.save_name, self.list)
