def __init__(
self,
args,
mode,
dataset_path="./datasets",
crop_dim=(32, 32, 32),
split_id=1,
samples=1000,
load=False,
):
# split_id = int(split_id)
fold_id = int(args.fold_id)
print(f"using fold_id {fold_id}")
"""
: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 = int(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")))
print(self.training_path)
self.affine = img_loader.load_affine_matrix(list_IDsT1[0])
if self.mode == "train":
# custom code
# list_IDsT1 = list_IDsT1[:split_id]
# list_IDsT2 = list_IDsT2[:split_id]
# labels = labels[:split_id]
list_IDsT1 = [x for x in list_IDsT1 if f"-{fold_id}-" not in x]
list_IDsT2 = [x for x in list_IDsT2 if f"-{fold_id}-" not in x]
labels = [x for x in labels if f"-{fold_id}-" not in x]
assert len(labels) == len(list_IDsT1)
assert len(labels) == len(list_IDsT2)
assert len(labels) == 9
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:]
list_IDsT1 = [x for x in list_IDsT1 if f"-{fold_id}-" in x]
list_IDsT2 = [x for x in list_IDsT2 if f"-{fold_id}-" in x]
labels = [x for x in labels if f"-{fold_id}-" in x]
assert len(labels) == len(list_IDsT1)
assert len(labels) == len(list_IDsT2)
assert len(labels) == 1
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
list_IDsT1 = [x for x in list_IDsT1 if f"-{fold_id}-" in x]
list_IDsT2 = [x for x in list_IDsT2 if f"-{fold_id}-" in x]
labels = [x for x in labels if f"-{fold_id}-" in x]
assert len(labels) == len(list_IDsT1)
assert len(labels) == len(list_IDsT2)
assert len(labels) == 1
self.list = create_non_overlapping_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")
utils.save_list(self.save_name, self.list)
def __init__(
self,
args,
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.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 = os.path.join(
self.root, "brats2019", f"brats2019-list-{mode}-samples-{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, "*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)
# split HGG and LGG
HGG_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, "HGG/*/*t1.nii.gz"))
)
HGG_IDsT1ce = sorted(
glob.glob(os.path.join(self.training_path, "HGG/*/*t1ce.nii.gz"))
)
HGG_IDsT2 = sorted(
glob.glob(os.path.join(self.training_path, "HGG/*/*t2.nii.gz"))
)
HGG_IDsFlair = sorted(
glob.glob(os.path.join(self.training_path, "HGG/*/*_flair.nii.gz"))
)
HGG_labels = sorted(
glob.glob(os.path.join(self.training_path, "HGG/*/*_seg.nii.gz"))
)
LGG_IDsT1 = sorted(
glob.glob(os.path.join(self.training_path, "LGG/*/*t1.nii.gz"))
)
LGG_IDsT1ce = sorted(
glob.glob(os.path.join(self.training_path, "LGG/*/*t1ce.nii.gz"))
)
LGG_IDsT2 = sorted(
glob.glob(os.path.join(self.training_path, "LGG/*/*t2.nii.gz"))
)
LGG_IDsFlair = sorted(
glob.glob(os.path.join(self.training_path, "LGG/*/*_flair.nii.gz"))
)
LGG_labels = sorted(
glob.glob(os.path.join(self.training_path, "LGG/*/*_seg.nii.gz"))
)
(
HGG_IDsT1,
HGG_IDsT1ce,
HGG_IDsT2,
HGG_IDsFlair,
HGG_labels,
) = utils.shuffle_lists(
HGG_IDsT1, HGG_IDsT1ce, HGG_IDsT2, HGG_IDsFlair, HGG_labels, seed=17
)
(
LGG_IDsT1,
LGG_IDsT1ce,
LGG_IDsT2,
LGG_IDsFlair,
LGG_labels,
) = utils.shuffle_lists(
LGG_IDsT1, LGG_IDsT1ce, LGG_IDsT2, LGG_IDsFlair, LGG_labels, seed=17
)
self.affine = img_loader.load_affine_matrix((HGG_IDsT1 + LGG_IDsT1)[0])
hgg_len = len(HGG_IDsT1)
lgg_len = len(LGG_IDsT1)
print("Brats2019, Training HGG:", hgg_len)
print("Brats2019, Training LGG:", lgg_len)
print("Brats2019, Training total:", hgg_len + lgg_len)
hgg_split = int(hgg_len * 0.8)
lgg_split = int(lgg_len * 0.8)
if self.mode == "train":
list_IDsT1 = HGG_IDsT1[:hgg_split] + LGG_IDsT1[:hgg_split]
list_IDsT1ce = HGG_IDsT1ce[:hgg_split] + LGG_IDsT1ce[:hgg_split]
list_IDsT2 = HGG_IDsT2[:hgg_split] + LGG_IDsT2[:hgg_split]
list_IDsFlair = HGG_IDsFlair[:hgg_split] + LGG_IDsFlair[:hgg_split]
labels = HGG_labels[:hgg_split] + LGG_labels[:hgg_split]
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,
)
elif self.mode == "val":
list_IDsT1 = HGG_IDsT1[hgg_split:] + LGG_IDsT1[hgg_split:]
list_IDsT1ce = HGG_IDsT1ce[hgg_split:] + LGG_IDsT1ce[hgg_split:]
list_IDsT2 = HGG_IDsT2[hgg_split:] + LGG_IDsT2[hgg_split:]
list_IDsFlair = HGG_IDsFlair[hgg_split:] + LGG_IDsFlair[hgg_split:]
labels = HGG_labels[hgg_split:] + LGG_labels[hgg_split:]
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,
)
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
list_IDsT1 = HGG_IDsT1[hgg_split:] + LGG_IDsT1[hgg_split:]
list_IDsT1ce = HGG_IDsT1ce[hgg_split:] + LGG_IDsT1ce[hgg_split:]
list_IDsT2 = HGG_IDsT2[hgg_split:] + LGG_IDsT2[hgg_split:]
list_IDsFlair = HGG_IDsFlair[hgg_split:] + LGG_IDsFlair[hgg_split:]
labels = HGG_labels[hgg_split:] + LGG_labels[hgg_split:]
self.list = create_non_overlapping_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,
)
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):
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.threshold = args.threshold
self.normalization = args.normalization
self.augmentation = args.augmentation
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(
self.save_name = self.root + '/iseg2017-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_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,
th_percent=self.threshold,
normalization=args.normalization)
elif self.mode == 'val':
utils.make_dirs(self.sub_vol_path)
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,
th_percent=self.threshold,
normalization=args.normalization)
self.full_volume = get_viz_set(list_IDsT1,
list_IDsT2,
labels,
dataset_name="iseg2017")
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
elif self.mode == 'viz':
list_IDsT1 = list_IDsT1[split_id:]
list_IDsT2 = list_IDsT2[:split_id:]
labels = labels[split_id:]
self.full_volume = get_viz_set(list_IDsT1,
list_IDsT2,
labels,
dataset_name="iseg2017")
self.list = []
utils.save_list(self.save_name, self.list)