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',
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,
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)
from lib.medloaders.medical_loader_utils import generate_padded_subvolumes
import torch
import matplotlib.pyplot as plt
import lib.augment3D as augment
size = 32
from lib.medloaders.medical_image_process import load_medical_image
# t1 = torch.randn(size,size,size).numpy()
# t2 = torch.randn(size,size,size).numpy()
b = torch.randn(size, size, size).numpy()
t1 = load_medical_image(
'.././datasets/iseg_2017/iSeg-2017-Training/subject-1-T1.hdr').squeeze(
).numpy()
label = load_medical_image(
'.././datasets/iseg_2017/iSeg-2017-Training/subject-1-label.img').squeeze(
).numpy()
f, axarr = plt.subplots(4, 1)
axarr[0].imshow(t1[70, :, :])
axarr[1].imshow(label[70, :, :])
c = augment.RandomChoice(transforms=[augment.GaussianNoise(mean=0, std=0.1)])
[t1], label = c([t1], label)
axarr[2].imshow(t1[70, :, :])
axarr[3].imshow(label[70, :, :])
plt.show()
