def get_stripped_mip():
"""
A script to get stripped and segmented MIP images to then do analysis on
:return:
"""
configure_logger()
client = cloud.authenticate()
bucket = client.get_bucket('elvos')
# for every blob
for in_blob in bucket.list_blobs(prefix=PREFIX):
# perform the normal MIPing procedure
logging.info(f"downloading {in_blob.name}")
input_arr = cloud.download_array(in_blob)
logging.info(f"blob shape: {input_arr.shape}")
cropped_arr = transforms.crop_strip_skull(input_arr, LOCATION)
logging.info("mipping numpy array")
mip_arr = transforms.mip_normal(cropped_arr)
# strip skull and grey matter to segment blood vessels
logging.info("segment blood vessels")
stripped_arr = transforms.segment_vessels(mip_arr, LOCATION)
# save to cloud
save_to_cloud(stripped_arr, in_blob)
def get_stripped_mip():
for location in LOCATIONS:
prefix = location + '/'
for in_blob in set_cloud().list_blobs(prefix=prefix):
# test: only using one patient's mipped scans for now
# if in_blob.name != prefix + '0RB9KGMO90G1YQZD.npy':
# continue
# perform the normal MIPing procedure
logging.info(f'downloading {in_blob.name}')
input_arr = cloud.download_array(in_blob)
logging.info(f"blob shape: {input_arr.shape}")
cropped_arr = transforms.crop_strip_skull(input_arr, location)
logging.info(f"mipping numpy array")
mip_arr = transforms.mip_normal(cropped_arr)
print(mip_arr)
# strip skull and segment blood vessels
logging.info(f"segment blood vessels")
stripped_arr = transforms.segment_vessels(mip_arr, location)
print(stripped_arr)
get_og_mip(cropped_arr)
plt.figure(figsize=(6, 6))
plt.imshow(stripped_arr, interpolation='none')
plt.show()
def get_og_mip(cropped_arr: np.ndarray):
# perform the normal MIPing procedure
not_extreme_arr = transforms.remove_extremes(cropped_arr)
logging.info(f'removed array extremes')
mip_arr = transforms.mip_normal(not_extreme_arr)
plt.figure(figsize=(6, 6))
plt.imshow(mip_arr, interpolation='none')
plt.show()
def get_og_mip(cropped_arr: np.ndarray):
"""
Gets and shows normal MIP for comparison
:param cropped_arr: array to MIP
:return:
"""
# perform the normal MIPing procedure
not_extreme_arr = transforms.remove_extremes(cropped_arr)
logging.info("removed array extremes")
mip_arr = transforms.mip_normal(not_extreme_arr)
plt.figure(figsize=(6, 6))
plt.imshow(mip_arr, interpolation='none')
plt.show()
def get_stripped_mip():
configure_logger()
client = cloud.authenticate()
bucket = client.get_bucket('elvos')
for in_blob in bucket.list_blobs(prefix=prefix):
# perform the normal MIPing procedure
logging.info(f"downloading {in_blob.name}")
input_arr = cloud.download_array(in_blob)
logging.info(f"blob shape: {input_arr.shape}")
cropped_arr = transforms.crop_strip_skull(input_arr, location)
logging.info("mipping numpy array")
mip_arr = transforms.mip_normal(cropped_arr)
# strip skull and grey matter to segment blood vessels
logging.info("segment blood vessels")
stripped_arr = transforms.segment_vessels(mip_arr, location)
save_to_cloud(stripped_arr, in_blob)