def apply(image,
model=None,
force_cpu=False,
batch_size=20,
volume_postprocessing=True,
show_process=True):
if model is None:
model = get_model('unet', 'R231')
voxvol = np.prod(image.GetSpacing())
inimg_raw = sitk.GetArrayFromImage(image)
del image
if force_cpu:
device = torch.device('cpu')
else:
if torch.cuda.is_available():
device = torch.device('cuda')
else:
logging.info(
"No GPU support available, will use CPU. Note, that this is significantly slower!"
)
batch_size = 1
device = torch.device('cpu')
model.to(device)
tvolslices, xnew_box = utils.preprocess(inimg_raw, resolution=[256, 256])
tvolslices[tvolslices > 600] = 600
tvolslices = np.divide((tvolslices + 1024), 1624)
torch_ds_val = utils.LungLabelsDS_inf(tvolslices)
dataloader_val = torch.utils.data.DataLoader(torch_ds_val,
batch_size=batch_size,
shuffle=False,
num_workers=1,
pin_memory=False)
timage_res = np.empty((np.append(0, tvolslices[0].shape)), dtype=np.uint8)
with torch.no_grad():
for X in tqdm(dataloader_val):
X = X.float().to(device)
prediction = model(X)
pls = torch.max(prediction,
1)[1].detach().cpu().numpy().astype(np.uint8)
timage_res = np.vstack((timage_res, pls))
# postprocessing includes removal of small connected components, hole filling and mapping of small components to
# neighbors
if volume_postprocessing:
outmask = utils.postrocessing(timage_res)
else:
outmask = timage_res
outmask = np.asarray([
utils.reshape_mask(outmask[i], xnew_box[i], inimg_raw.shape[1:])
for i in range(outmask.shape[0])
],
dtype=np.uint8)
return outmask
def apply(image,
model=None,
force_cpu=False,
batch_size=20,
volume_postprocessing=True,
noHU=False,
verbose=False):
if model is None:
model = get_model('unet', 'R231')
inimg_raw = sitk.GetArrayFromImage(image)
directions = np.asarray(image.GetDirection())
if len(directions) == 9:
inimg_raw = np.flip(inimg_raw,
np.where(directions[[0, 4, 8]][::-1] < 0)[0])
del image
if force_cpu:
device = torch.device('cpu')
else:
if torch.cuda.is_available():
device = torch.device('cuda')
else:
logging.info(
"No GPU support available, will use CPU. Note, that this is significantly slower!"
)
batch_size = 1
device = torch.device('cpu')
model.to(device)
if not noHU:
tvolslices, xnew_box = utils.preprocess(inimg_raw,
resolution=[256, 256])
tvolslices[tvolslices > 600] = 600
tvolslices = np.divide((tvolslices + 1024), 1624)
else:
# support for non HU images. This is just a hack. The models were not trained with this in mind
tvolslices = skimage.color.rgb2gray(inimg_raw)
tvolslices = skimage.transform.resize(tvolslices, [256, 256])
tvolslices = np.asarray(
[tvolslices * x for x in np.linspace(0.3, 2, 20)])
tvolslices[tvolslices > 1] = 1
sanity = [(tvolslices[x] > 0.6).sum() > 25000
for x in range(len(tvolslices))]
tvolslices = tvolslices[sanity]
torch_ds_val = utils.LungLabelsDS_inf(tvolslices)
dataloader_val = torch.utils.data.DataLoader(torch_ds_val,
batch_size=batch_size,
shuffle=False,
num_workers=1,
pin_memory=False)
timage_res = np.empty((np.append(0, tvolslices[0].shape)), dtype=np.uint8)
with torch.no_grad():
for X in tqdm(dataloader_val, disable=not verbose):
X = X.float().to(device)
prediction = model(X)
pls = torch.max(prediction,
1)[1].detach().cpu().numpy().astype(np.uint8)
timage_res = np.vstack((timage_res, pls))
# postprocessing includes removal of small connected components, hole filling and mapping of small components to
# neighbors
if volume_postprocessing:
outmask = utils.postrocessing(timage_res)
else:
outmask = timage_res
if noHU:
outmask = skimage.transform.resize(outmask[np.argmax(
(outmask == 1).sum(axis=(1, 2)))],
inimg_raw.shape[:2],
order=0,
anti_aliasing=False,
preserve_range=True)[None, :, :]
else:
outmask = np.asarray([
utils.reshape_mask(outmask[i], xnew_box[i], inimg_raw.shape[1:])
for i in range(outmask.shape[0])
],
dtype=np.uint8)
if len(directions) == 9:
outmask = np.flip(outmask,
np.where(directions[[0, 4, 8]][::-1] < 0)[0])
return outmask.astype(np.uint8)