print(
'generating synthetic data to {} (this may take a while)'.format(tempdir))
for i in range(5):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
# Define transforms for image and segmentation
imtrans = transforms.Compose([Rescale(), AddChannel()])
segtrans = transforms.Compose([AddChannel()])
ds = NiftiDataset(images,
segs,
transform=imtrans,
seg_transform=segtrans,
image_only=False)
device = torch.device("cuda:0")
net = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
for i in range(50):
im, seg = create_test_image_3d(256, 256, 256)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
# Define transforms for image and segmentation
imtrans = transforms.Compose(
[Rescale(),
AddChannel(),
UniformRandomPatch((64, 64, 64)),
ToTensor()])
segtrans = transforms.Compose(
[AddChannel(), UniformRandomPatch((64, 64, 64)),
ToTensor()])
# Define nifti dataset, dataloader.
ds = NiftiDataset(images, segs, transform=imtrans, seg_transform=segtrans)
loader = DataLoader(ds,
batch_size=10,
num_workers=2,
pin_memory=torch.cuda.is_available())
im, seg = monai.utils.misc.first(loader)
print(im.shape, seg.shape)
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
val_files = [{
'img': img,
'label': label
} for img, label in zip(images, labels)]
# Define transforms for image
val_transforms = transforms.Compose([
LoadNiftid(keys=['img']),
AddChanneld(keys=['img']),
Rescaled(keys=['img']),
Resized(keys=['img'], output_spatial_shape=(96, 96, 96))
])
# Create DenseNet121
net = monai.networks.nets.densenet3d.densenet121(
in_channels=1,
out_channels=2,
)
device = torch.device("cuda:0")
def prepare_batch(batch, device=None, non_blocking=False):
return _prepare_batch((batch['img'], batch['label']), device, non_blocking)
channel_dim=-1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
val_files = [{'img': img, 'seg': seg} for img, seg in zip(images, segs)]
# Define transforms for image and segmentation
val_transforms = transforms.Compose([
LoadNiftid(keys=['img', 'seg']),
AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1),
Rescaled(keys=['img', 'seg'])
])
val_ds = monai.data.Dataset(data=val_files, transform=val_transforms)
device = torch.device("cuda:0")
net = UNet(
dimensions=3,
in_channels=1,
out_channels=1,
channels=(16, 32, 64, 128, 256),
strides=(2, 2, 2, 2),
num_res_units=2,
)
net.to(device)
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
train_files = [{
'img': img,
'label': label
} for img, label in zip(images[:10], labels[:10])]
val_files = [{
'img': img,
'label': label
} for img, label in zip(images[-10:], labels[-10:])]
# Define transforms for image
train_transforms = transforms.Compose([
LoadNiftid(keys=['img']),
AddChanneld(keys=['img']),
Rescaled(keys=['img']),
Resized(keys=['img'], output_spatial_shape=(96, 96, 96)),
RandRotate90d(keys=['img'], prob=0.8, spatial_axes=[0, 2])
])
val_transforms = transforms.Compose([
LoadNiftid(keys=['img']),
AddChanneld(keys=['img']),
Rescaled(keys=['img']),
Resized(keys=['img'], output_spatial_shape=(96, 96, 96))
])
# Define dataset, dataloader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=4,
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
# Define transforms
train_transforms = transforms.Compose(
[Rescale(), AddChannel(),
Resize((96, 96, 96)),
RandRotate90()])
val_transforms = transforms.Compose(
[Rescale(), AddChannel(), Resize((96, 96, 96))])
# Define nifti dataset, dataloader
check_ds = NiftiDataset(image_files=images,
labels=labels,
transform=train_transforms)
check_loader = DataLoader(check_ds,
batch_size=2,
num_workers=2,
pin_memory=torch.cuda.is_available())
im, label = monai.utils.misc.first(check_loader)
print(type(im), im.shape, label)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'img%i.nii.gz' % i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
images = sorted(glob(os.path.join(tempdir, 'img*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
train_files = [{'img': img, 'seg': seg} for img, seg in zip(images[:20], segs[:20])]
val_files = [{'img': img, 'seg': seg} for img, seg in zip(images[-20:], segs[-20:])]
# Define transforms for image and segmentation
train_transforms = transforms.Compose([
LoadNiftid(keys=['img', 'seg']),
AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1),
Rescaled(keys=['img', 'seg']),
RandCropByPosNegLabeld(keys=['img', 'seg'], label_key='seg', size=[96, 96, 96], pos=1, neg=1, num_samples=4),
RandRotate90d(keys=['img', 'seg'], prob=0.8, spatial_axes=[0, 2])
])
val_transforms = transforms.Compose([
LoadNiftid(keys=['img', 'seg']),
AsChannelFirstd(keys=['img', 'seg'], channel_dim=-1),
Rescaled(keys=['img', 'seg'])
])
# Define dataset, dataloader
check_ds = monai.data.Dataset(data=train_files, transform=train_transforms)
# use batch_size=2 to load images and use RandCropByPosNegLabeld to generate 2 x 4 images for network training
check_loader = DataLoader(check_ds, batch_size=2, num_workers=4, collate_fn=list_data_collate,
pin_memory=torch.cuda.is_available())
check_data = monai.utils.misc.first(check_loader)
'generating synthetic data to {} (this may take a while)'.format(tempdir))
for i in range(40):
im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1)
n = nib.Nifti1Image(im, np.eye(4))
nib.save(n, os.path.join(tempdir, 'im%i.nii.gz' % i))
n = nib.Nifti1Image(seg, np.eye(4))
nib.save(n, os.path.join(tempdir, 'seg%i.nii.gz' % i))
images = sorted(glob(os.path.join(tempdir, 'im*.nii.gz')))
segs = sorted(glob(os.path.join(tempdir, 'seg*.nii.gz')))
# Define transforms for image and segmentation
train_imtrans = transforms.Compose(
[Rescale(), AddChannel(),
UniformRandomPatch((96, 96, 96))])
train_segtrans = transforms.Compose(
[AddChannel(), UniformRandomPatch((96, 96, 96))])
val_imtrans = transforms.Compose(
[Rescale(), AddChannel(), Resize((96, 96, 96))])
val_segtrans = transforms.Compose([AddChannel(), Resize((96, 96, 96))])
# Define nifti dataset, dataloader
check_ds = NiftiDataset(images,
segs,
transform=train_imtrans,
seg_transform=train_segtrans)
check_loader = DataLoader(check_ds,
batch_size=10,
num_workers=2,
"/workspace/data/medical/ixi/IXI-T1/IXI607-Guys-1097-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI175-HH-1570-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI385-HH-2078-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI344-Guys-0905-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI409-Guys-0960-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI584-Guys-1129-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI253-HH-1694-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI092-HH-1436-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI574-IOP-1156-T1.nii.gz",
"/workspace/data/medical/ixi/IXI-T1/IXI585-Guys-1130-T1.nii.gz"
]
# 2 binary labels for gender classification: man and woman
labels = np.array([0, 0, 1, 0, 1, 0, 1, 0, 1, 0])
# Define transforms for image
val_transforms = transforms.Compose(
[Rescale(), AddChannel(), Resize((96, 96, 96))])
# Define nifti dataset
val_ds = NiftiDataset(image_files=images,
labels=labels,
transform=val_transforms,
image_only=False)
# Create DenseNet121
net = monai.networks.nets.densenet3d.densenet121(
in_channels=1,
out_channels=2,
)
device = torch.device("cuda:0")
metric_name = 'Accuracy'
# add evaluation metric to the evaluator engine
val_metrics = {metric_name: Accuracy()}