def getMaskVolumes():
csvLines = utils.readCsv("../trainset_csv/trainNodules_gt.csv")
last_ID = 0
scan = 0
spacing = 0
origin = 0
maskVolumesList = []
# ignore header
for line in csvLines[1:]:
# get image of this patient only one time (there are repeated patient ids)
current_ID = line[0]
if last_ID != current_ID:
print(getFileID(current_ID))
scan, spacing, origin, _ = utils.readMhd(
'../LNDb dataset/dataset/LNDb-' + getFileID(current_ID) +
'.mhd')
spacing = [float(spacing[i]) for i in range(3)]
# find the coordinates of the current nodule (it is done for every line of the csv)
finding_coords = line[4:7]
nodule_x = (float(finding_coords[0]) - float(origin[0])) / float(
spacing[0])
nodule_y = (float(finding_coords[1]) - float(origin[1])) / float(
spacing[1])
nodule_z = (float(finding_coords[2]) - float(origin[2])) / float(
spacing[2])
real_coords = [nodule_x, nodule_y, nodule_z]
# get a mask for the image of this patient (from one of the radiologists that found the current nodule)
radiologists = line[
1] # list of radiologists that found the current nodule
radId = str(
radiologists[0]
) # always choose the mask from the first radiologist in the list
mask, _, _, _ = utils.readMhd('../LNDb dataset/masks/LNDb-' +
getFileID(current_ID) + '_rad' + radId +
'.mhd')
# extract mini cube of the current nodule on the masked scan
mask_volume = utils.extractCube(mask,
spacing,
real_coords,
cube_size=80)
# add mask volumes to the list
maskVolumesList.append(mask_volume)
last_ID = current_ID
return maskVolumesList
def load_image(img_name, centers):
''' loads a single image and returns a list of subimages (cubes around nodule-like
findings)
img_name: valid path to an image from current working directory
centers: a list of the centroids of nodule-like occurrences in img
in world coordinates
'''
scan, spacing, origin, transfmat = readMhd(img_name)
transfmat_toimg, _ = getImgWorldTransfMats(spacing,
transfmat) # just need image
centers = [convertToImgCoord(c, origin, transfmat_toimg) for c in centers]
subimages = [extractCube(scan, spacing, c) for c in centers]
return subimages
def createCube(cubeSize=80):
csvLines = readCsv("csv/trainSet.csv")
header = csvLines[0]
nodules = csvLines[1:]
for i, n in tqdm(enumerate(nodules[:1])):
x = int(n[header.index("x")])
y = int(n[header.index("y")])
z = int(n[header.index("z")])
lnd = int(n[header.index("LNDbID")])
ctr = np.array([x, y, z])
[scan, spacing, _, _] = readMhd('data/LNDb-{:04}.mhd'.format(lnd))
cube = extractCube(scan, spacing, ctr, cubeSize)
np.save("cube/{:0}.npy".format(i), cube)
def getCubes(cubeSize):
csvLines = utils.readCsv("../trainset_csv/trainNodules_gt.csv")
last_ID = 0
scan = 0
spacing = 0
origin = 0
cubeList = []
textures = [row[-1] for row in csvLines]
# delete 1st element (header)
del textures[0]
# ignore header
for line in csvLines[1:]:
current_ID = line[0]
if last_ID != current_ID:
print(getFileID(current_ID))
scan,spacing,origin,_ = utils.readMhd('../LNDb dataset/dataset/LNDb-' + getFileID(current_ID) + '.mhd')
spacing = [float(spacing[i]) for i in range(3)]
finding_coords = line[4:7]
nodule_x = (float(finding_coords[0]) - float(origin[0])) / float(spacing[0])
nodule_y = (float(finding_coords[1]) - float(origin[1])) / float(spacing[1])
nodule_z = (float(finding_coords[2]) - float(origin[2])) / float(spacing[2])
real_coords = [nodule_x, nodule_y, nodule_z]
scan_cube = utils.extractCube(scan, spacing, real_coords, cube_size=cubeSize)
cubeList.append(scan_cube)
# nodule_coords
last_ID = current_ID
return cubeList, textures
# Read scan
if lnd!=lndloaded:
[scan,spacing,origin,transfmat] = readMhd('/media/tungthanhlee/SSD/grand_challenge/dataset/LNDB_segmentation/data/LNDb-{:04}.mhd'.format(lnd))
transfmat_toimg,transfmat_toworld = getImgWorldTransfMats(spacing,transfmat)
lndloaded = lnd
# Convert coordinates to image
ctr = convertToImgCoord(ctr,origin,transfmat_toimg)
# for rad,radfinding in zip(rads,radfindings):
# Read segmentation mask
[mask,_,_,_] = readMhd('/media/tungthanhlee/SSD/grand_challenge/dataset/LNDB_segmentation/masks/LNDb-{:04}_rad{}.mhd'.format(lnd,rad))
# Extract cube around nodule
scan_cube = extractCube(scan,spacing,ctr)
masknod = copy.copy(mask)
# masknod[masknod!=radfinding] = 0
masknod[masknod!=finding] = 0
masknod[masknod>0] = 1
mask_cube = extractCube(masknod,spacing,ctr)
# Display mid slices from resampled scan/mask
if dispFlag:
fig, axs = plt.subplots(2,3)
axs[0,0].imshow(scan_cube[int(scan_cube.shape[0]/2),:,:])
axs[1,0].imshow(mask_cube[int(mask_cube.shape[0]/2),:,:])
axs[0,1].imshow(scan_cube[:,int(scan_cube.shape[1]/2),:])
axs[1,1].imshow(mask_cube[:,int(mask_cube.shape[1]/2),:])
axs[0,2].imshow(scan_cube[:,:,int(scan_cube.shape[2]/2)])
axs[1,2].imshow(mask_cube[:,:,int(mask_cube.shape[2]/2)])
if lnd != lndloaded:
[scan, spacing, origin,
transfmat] = readMhd('data/LNDb-{:04}.mhd'.format(lnd))
transfmat_toimg, transfmat_toworld = getImgWorldTransfMats(
spacing, transfmat)
lndloaded = lnd
# Convert coordinates to image
ctr = convertToImgCoord(ctr, origin, transfmat_toimg)
#for rad,radfinding in zip(rads,radfindings):
# Read segmentation mask
#[mask,_,_,_] = readMhd('masks/LNDb-{:04}_rad{}.mhd'.format(lnd,rad))
# Extract cube around nodule
scan_cube = extractCube(scan, spacing, ctr)
"""masknod = copy.copy(mask)
masknod[masknod!=radfinding] = 0
masknod[masknod>0] = 1
mask_cube = extractCube(masknod,spacing,ctr)"""
slice1 = scan_cube[int(scan_cube.shape[0] / 2), :, :]
slice2 = scan_cube[:, int(scan_cube.shape[1] / 2), :]
slice3 = scan_cube[:, :, int(scan_cube.shape[2] / 2)]
# Display mid slices from resampled scan/mask
if dispFlag:
fig, axs = plt.subplots(2, 3)
axs[0, 0].imshow(slice1)
axs[1, 0].imshow(mask_cube[int(mask_cube.shape[0] / 2), :, :])
axs[0, 1].imshow(slice2)
axs[1, 1].imshow(mask_cube[:, int(mask_cube.shape[1] / 2), :])
if lnd != lndloaded:
[scan, spacing, origin,
transfmat] = readMhd('../Dataset/LNDb-{:04}.mhd'.format(lnd))
transfmat_toimg, transfmat_toworld = getImgWorldTransfMats(
spacing, transfmat)
lndloaded = lnd
# Convert coordinates to image
ctr = convertToImgCoord(ctr, origin, transfmat_toimg)
# Read segmentation mask
[mask, _, _,
_] = readMhd('../Dataset/masks/LNDb-{:04}_rad{}.mhd'.format(lnd, rad))
# Extract cube around nodule
scan_cube = extractCube(scan, spacing, ctr)
masknod = copy.copy(mask)
masknod[masknod != finding] = 0 #is radfinding on other file.
masknod[masknod > 0] = 1
mask_cube = extractCube(masknod, spacing, ctr, cube_size, voxel_size)
print(mask_cube)
# Display mid slices from resampled scan/mask
if dispFlag:
fig, axs = plt.subplots(2, 3)
axs[0, 0].imshow(scan_cube[int(scan_cube.shape[0] / 2), :, :])
axs[1, 0].imshow(mask_cube[int(mask_cube.shape[0] / 2), :, :])
axs[0, 1].imshow(scan_cube[:, int(scan_cube.shape[1] / 2), :])
axs[1, 1].imshow(mask_cube[:, int(mask_cube.shape[1] / 2), :])
transfmat] = readMhd('data/LNDb-{:04}.mhd'.format(lnd))
transfmat_toimg, transfmat_toworld = getImgWorldTransfMats(
spacing, transfmat)
lndloaded = lnd
# Convert coordinates to image
#------------------------------------------------------------------------------
ctr = convertToImgCoord(ctr, origin, transfmat_toimg)
OriginalCTR = ctr
for rad, radfinding in zip(rads, radfindings):
# Read segmentation mask
[mask, _, _,
_] = readMhd('masks/LNDb-{:04}_rad{}.mhd'.format(lnd, rad))
# Extract cube around nodule
scan_cube = extractCube(scan, spacing, ctr)
masknod = copy.copy(mask)
masknod[masknod != radfinding] = 0
masknod[masknod > 0] = 1
mask_cube = extractCube(masknod, spacing, ctr)
img1 = mask_cube[int(mask_cube.shape[0] / 2), :, :]
img2 = mask_cube[:, int(mask_cube.shape[1] / 2), :]
img3 = mask_cube[:, :, int(mask_cube.shape[2] / 2)]
XValues = []
YValues = []
ZValues = []
XLayers = []
YLayers = []
ZLayers = []
E = 0
fil, col = img1.shape
