def main(rabbit):
import RabbitCommon as rc
in_path = f'/scratch/rabbit_data/{rabbit}/rawDicoms/ExVivo*/*'
out_path = f'/hdscratch/ucair/{rabbit}/mri/exvivo/volumes/raw/'
files = sorted(glob.glob(in_path))
if not os.path.exists(out_path):
os.makedirs(out_path)
for cnt, dcm_file in enumerate(files):
print(f'Processing {dcm_file} ... ', end='')
out_name = dcm_file.split('/')[-1].split(' ')[-1]
if os.path.exists(f'{out_path}{cnt:02d}_{out_name}.nii.gz'):
print('done')
continue
vol = rc.LoadDICOM(dcm_file, 'cpu')
io.SaveITKFile(vol, f'{out_path}{cnt:02d}_{out_name}.nii.gz')
print('done')
# Copy over some of the 'invivo' (day3)
out_path = f'/hdscratch/ucair/{rabbit}/mri/invivo/volumes/raw/'
in_path = f'/scratch/rabbit_data/{rabbit}/rawVolumes/Post*/*'
files = sorted(glob.glob(in_path))
filt_files = [x for x in files if '3D_VIBE_0.5' in x]
filt_files += [x for x in files if 't2' in x]
filt_files += [x for x in files if 'Day3' in x]
if not os.path.exists(out_path):
os.makedirs(out_path)
for f in filt_files:
shutil.copy(f, f'{out_path}{f.split("/")[-1]}')
# Copy over some of the day0
out_path = f'/hdscratch/ucair/{rabbit}/mri/day0/volumes/raw/'
in_path = f'/scratch/rabbit_data/{rabbit}/rawVolumes/Ablation*/*'
files = sorted(glob.glob(in_path))
filt_files = [x for x in files if '3D_VIBE_0.5' in x]
filt_files += [x for x in files if 't2' in x]
filt_files += [x for x in files if 'Day0' in x]
if not os.path.exists(out_path):
os.makedirs(out_path)
for f in filt_files:
shutil.copy(f, f'{out_path}{f.split("/")[-1]}')
def save_volume(vol, opt):
import CAMP.Core as core
import CAMP.FileIO as io
# Bring the volume to the CPU
vol = vol.to('cpu')
out_path = f'{opt.hd_root}/{opt.rabbit}/blockface/{opt.block}/volumes/raw/'
mhd_file = f'{out_path}/surface_volume.mhd'
mhd_dict = read_mhd_header(mhd_file)
data_files = f'predictions/IMG_%03d_prediction.raw 0 {vol.shape[0]} 1'
mhd_dict['ElementDataFile'] = data_files
mhd_dict['ElementNumberOfChannels'] = 1
out_path = f'{out_path}/predictions/'
try:
os.stat(out_path)
except OSError:
os.makedirs(out_path)
origin = [float(x) for x in mhd_dict['Offset'].split(' ')][0:2]
spacing = [float(x) for x in mhd_dict['ElementSpacing'].split(' ')][0:2]
# Loop over the firs dimension
for s in range(0, vol.shape[0]):
v_slice = core.StructuredGrid(
vol.shape[1:],
tensor=vol[s, :, :].unsqueeze(0),
spacing=spacing,
origin=origin,
device='cpu',
dtype=torch.float32,
channels=1
)
# Generate the name for the image
out_name = f'{out_path}IMG_{s:03d}_prediction.mhd'
io.SaveITKFile(v_slice, out_name)
write_mhd_header(f'{out_path}../prediction_volume.mhd', mhd_dict)
}
evalOpt = SimpleNamespace(**evalOpt)
trainOpt = SimpleNamespace(**trainOpt)
# train(trainOpt)
block_list = sorted(glob.glob(f'{evalOpt.hd_root}{evalOpt.rabbit}/blockface/block*'))
for block_path in block_list[1:]:
block = block_path.split('/')[-1]
print(f'{block} ... ')
import CAMP.Core as core
import CAMP.FileIO as io
scat_vol = io.LoadITKFile(f'{block_path}/volumes/raw/scatter_volume.mhd', device='cuda')
grad_scat = scat_vol.data[:, :-1, :, :].cpu() - scat_vol.data[:, 1:, :, :].cpu()
grad_scat = (grad_scat - grad_scat.min()) / (grad_scat.max() - grad_scat.min())
grad_scat = torch.cat([grad_scat[:, 0, :, :].unsqueeze(1), grad_scat], dim=1)
grad_scat = core.StructuredGrid(
size=scat_vol.size,
spacing=scat_vol.spacing,
origin=scat_vol.origin,
tensor=grad_scat[2].unsqueeze(0),
device='cuda',
channels=1
)
io.SaveITKFile(grad_scat, f'{block_path}/volumes/raw/scatter_gradient_volume.mhd')
evalOpt.block = block
eval(evalOpt)
print(f'{block} ... done')
print('All Done')