def is_point_partof_met(point):
point_is_partof_met = False
point_as_list = [point]
for met in mets_of_all_patches:
met_center = met['location']['center']
distance = blobanalysis.point_dist(met_center, point)
if distance > ROI_width: continue
points_of_met_rel = met['points']
patchID_of_patch_of_met = met['patch_id']
patch_of_met = dc.filter_dicts(whole_scan_metadata['patches'], 'id',
patchID_of_patch_of_met)[0]
offset_of_patch_of_met = patch_of_met['offset']
points_of_met = np.add(points_of_met_rel,
offset_of_patch_of_met).tolist()
point_is_partof_met = blobanalysis.test_overlap(
point_as_list, points_of_met)
if point_is_partof_met:
print('Point ' + str(point) + ' is part of met ' +
str(met['global_id']))
break
return point_is_partof_met
def get_patch_ID_given_offset(offset):
offset = np.array(offset, dtype=np.int64)
patches = dc.filter_dicts(whole_scan_metadata['patches'], 'offset', offset)
patch = patches[0]
patch_ID = patch['id']
return patch_ID
def get_whole_mouse_thumbnail(candidate_ID):
current_metastasis = get_current_metastasis(candidate_ID)
patch_ID = current_metastasis['patch_id']
patches = region['patches']
patch = dataconversions.filter_dicts(patches, 'id', patch_ID)[0]
patchstep = patch['patchstep']
z_patchstep = patchstep[2]
whole_mouse_thumbnail = whole_mouse_thumbnails[:, :, z_patchstep]
return whole_mouse_thumbnail
def get_lower_left_corner(candidate_ID):
current_metastasis = get_current_metastasis(candidate_ID)
patch_ID = current_metastasis['patch_id']
patches = region['patches']
patch = dataconversions.filter_dicts(patches, 'id', patch_ID)[0]
patchstep = patch['patchstep']
lower_left_x = patchstep[1] * 30
lower_left_y = patchstep[0] * 30
lower_left_corner = (lower_left_x, lower_left_y)
return lower_left_corner
def pick_sample():
samplecards = filehandling.pload(DATAPATH + '/mice_metadata/' + 'list_of_samplecards.pickledump')
mice = ['H2030IC10dn573','IC2dn2','IC6dn1','IC6dn2', 'IC14dn1', 'MCF7IC21dn528', 'PC14dn04595']
print()
print("Please choose a mouse:")
for m in enumerate(mice):
print('[', m[0], ']', ' ', m[1])
chosen_index = input('--> ')
mouse = mice[int(chosen_index)]
print()
print("What's the patch ID?")
patch_ID = input("--> ")
patch_ID = int(patch_ID)
print()
print("What's the metastasis ID?")
met_ID = input("--> ")
met_ID = int(met_ID)
samplecards_mouse = dataconversions.filter_dicts(samplecards, 'mouse', mouse)
samplecards_patch = dataconversions.filter_dicts(samplecards_mouse, 'patch_id', patch_ID)
samplecards_met = dataconversions.filter_dicts(samplecards_patch, 'met_id', met_ID)
chosen_samplecard = samplecards_met
if chosen_samplecard:
print()
print('Chosen sample:')
print(chosen_samplecard)
print()
sample_set = MetDataset(chosen_samplecard)
sample_loader = DataLoader(sample_set)
for i, (images, label) in enumerate(sample_loader):
return images
else:
return None
def is_met_in_ROI(met, ROI, ROI_center, is_ambig_met=False):
met_is_in_ROI = False
met_center = met['location']['center']
distance = blobanalysis.point_dist(met_center, ROI_center)
met_descr_string = 'Ambig-Met' if is_ambig_met else 'Met'
met_descr_string += ' ' + str(met['id']) + ' of Patch ' + str(
met['patch_id'])
# is
if distance < ROI_width_half:
met_is_in_ROI = True
# is or isn't
elif distance < (sqrt(3) * ROI_width_half + tolerance):
points_of_met_rel = met[
'points'] # pointlist of all points of met, relative to patch
patchID_of_patch_of_met = met['patch_id']
patch_of_met = dc.filter_dicts(whole_scan_metadata['patches'], 'id',
patchID_of_patch_of_met)[0]
offset_of_patch_of_met = patch_of_met['offset']
points_of_met = np.add(
points_of_met_rel, offset_of_patch_of_met).tolist(
) # pointlist of all points of met, abs. positions
points_of_ROI = get_points_of_ROI(
ROI, ROI_center) # pointlist of all points of ROI, abs. positions
# overlap_points = blobanalysis.get_overlap(points_of_met, points_of_ROI)
met_is_in_ROI = blobanalysis.test_overlap(points_of_met, points_of_ROI)
# isn't
else:
met_is_in_ROI = False
# if met_is_in_ROI: print(met_descr_string + ' is within ROI')
return met_is_in_ROI
def load_segmentation(dataset, pid, load_partial, THR_dim=200):
'''
segmentation = load_segmentation(dataset,pid,load_partial,THR_dim=200)
Not only loads segmentation file for desired patch, but also includes all (dim) metastases
that belong to adjacent patches if they partially overlap with this patch.
For those cases, only the overlapping part of the metastasis is included, i.e. this function
may NOT BE USED to derive statistical information on the metastases as this will be false
for exactly those partial metastases.
Also, if the part within the patch is small (less than 20% of total volume) or dim, it will
only be included as a "dim" metastasis, i.e. detection will not be rewarded and missing it
will not be punished; these metastases will have to be detected from the patch in which the
majority of its volume is in
Inputs:
- dataset: string specifying dataset ("F15")
- pid: integer for patch ID to be loaded
- load_partial: boolean whether or not to load partially overlapping blobs from surrounding patches
- THR_dim: threshold to check whether partial blobs are now considered dim because brighter parts are in other patch
'''
seg = filehandling.pload(BASEP + 'data/' + dataset +
'/segmentations/segmentation_patch_' + str(pid))
if (os.path.isfile('/mnt/C286054386053985/oschoppe/F15/patchvolume_' +
str(pid) + '.nii') == False):
print(
'WARNING: cannot load local patch files and thus, cannot consider partial blobs properly.'
)
print(
'--> function load_segmentation() will be called with load_partial=False instead'
)
print('ALTERED PATHS IN p_leo2 AND ALL HELPER FUNCTIONS!')
load_partial = False
if (load_partial):
region = filehandling.pload(BASEP + 'data/' + dataset + '/region')
[maxy, maxx,
maxz] = np.asarray(region['partitioning']['patch_size']
) - region['partitioning']['patch_overlap']
[y0, x0, z0] = region['patches'][pid]['patchstep']
cancervol = filehandling.readNifti(
'/mnt/C286054386053985/oschoppe/F15/patchvolume_' + str(pid))
add_part_metastases = []
add_part_dim_metastases = []
# Go through neighboring patches whose buffer zones overlap with patch
for dy in [-1, 0]:
for dx in [-1, 0]:
for dz in [-1, 0]:
[y, x, z] = [y0 + dy, x0 + dx, z0 + dz]
if ((y >= 0 and x >= 0 and z >= 0)
and ((y == y0 and x == x0 and z == z0) is False)):
npid = dataconversions.filter_dicts(
region['patches'], 'patchstep', [y, x, z])[0]['id']
nseg = filehandling.pload(
BASEP + 'data/' + dataset +
'/segmentations/segmentation_patch_' + str(npid))
# check whether any metastases overlap with patch
dim_metastases = [] if (
'dim_metastases'
not in nseg.keys()) else nseg['dim_metastases']
for m in nseg['metastases'] + dim_metastases:
abs_bb = m['offset'] + m['boundingbox']
if ((abs_bb[0] >= (-1) * dy * maxy)
and (abs_bb[1] >= (-1) * dx * maxx)
and (abs_bb[2] >= (-1) * dz * maxz)):
# if so, then include THOSE points WITHIN the patch & adjust coordinate system to new patch
patchstep_offset = np.multiply(
[maxy, maxx, maxz], [dy, dx, dz])
shifted_points = m['points'] + patchstep_offset
filtered_points = shifted_points[
np.min(shifted_points, axis=1) >=
0] # only take points fully within patch
if (len(filtered_points) > 0):
# characterize new partial blob
m_partial = {}
m_partial['id'] = 100 * npid + m['id']
m_partial[
'points'] = filtered_points.tolist()
m_partial = blobanalysis.characterize_blob(
m_partial) # ~ 0.1 s
m_partial = characterize_metastasis(
m_partial,
cancervol,
min_padding=25,
otherblobs=seg['metastases']
) # ~ 0.01 s
# add to main list if NOT dim and at least 20% within patch
if (m_partial['characterization']['maxFG']
> THR_dim and m_partial['volume']
>= 0.2 * m['volume']):
m_partial[
'INFO'] = 'Partial, but substantial metastases from adjacent patch #' + str(
npid)
add_part_metastases.append(m_partial)
else:
m_partial['evaluation'][
'flag_dim'] = True
m_partial[
'INFO'] = 'Partial, but dim/very small part of metastases from adjacent patch #' + str(
npid)
add_part_dim_metastases.append(
m_partial)
seg['metastases'] += add_part_metastases
if ('dim_metastases' in seg.keys()):
seg['dim_metastases'] += add_part_dim_metastases
return seg
fileToWriteTo = filepath + filename
plt.imsave(fileToWriteTo, MIP_x, format='png')
MIP_z = np.max(ROI, 2) # maximum intensity projection along z
axis = 'z'
filename = filename_prefix + '_' + axis + '.png'
fileToWriteTo = filepath + filename
plt.imsave(fileToWriteTo, MIP_z, format='png')
#%% Main
for mouse in mice:
prediction = filehandling.pload(DATAPATH + '/mice_metadata/' + mouse +
'/reviewed_prediction.pickledump')
candidates = prediction['metastases']
potential_TPs = dataconversions.filter_dicts(
candidates, 'evaluation-manually_confirmed', True)
confirmed_FPs = dataconversions.filter_dicts(
candidates, 'evaluation-manually_confirmed', False)
for potential_TP_met in potential_TPs:
for channel in channels:
print('\n\n')
print('### Mouse ', mouse, ' ###')
print('### Channel ', channel, ' ###')
ROI = crop_ROI(potential_TP_met, mouse, channel)
if ROI is not None:
filename_prefix = write_ROI_to_Nifti(ROI, potential_TP_met,
mouse, channel)
write_ROI_to_PNGs(ROI, filename_prefix)
def get_current_metastasis(candidate_ID):
current_metastasis = dataconversions.filter_dicts(TP_candidates,
'global_id',
candidate_ID)[0]
return current_metastasis
d = datetime.datetime.today()
DATE_OF_TODAY = d.strftime('%d-%m-%Y')
print("Annotator:\t", NAME_OF_ANNOTATOR)
print("Today's date:\t", DATE_OF_TODAY)
print("Mouse:\t\t", mouse)
print()
#%%
main_fig = plt.figure(num=101)
prediction = filehandling.pload(DATAPATH + '/mice_metadata/' + mouse +
'/reviewed_prediction.pickledump')
metastases = prediction['metastases']
TP_candidates = dataconversions.filter_dicts(metastases,
'evaluation-manually_confirmed',
True)
number_of_candidates = len(TP_candidates)
candidate_IDs = []
for TP_candidate in TP_candidates:
candidate_IDs.append(TP_candidate['global_id'])
all_candidate_IDs = copy.deepcopy(candidate_IDs)
print('All candidate IDs: ', all_candidate_IDs)
region = filehandling.pload(DATAPATH + '/mice_metadata/' + mouse +
'/region.pickledump')
whole_mouse_thumbnails = region['thumbnails']['MaxProjections_Z']
#%%
#%% Step 2) For each metastasis, add all relevant meta information
for m, metastasis in enumerate(segmentation['metastases']): # double loop as this will be needed for upcoming computations
print('Loop 1/2: Adding information for metastasis #' + str(metastasis['global_id']))
# add global location information to metastasis
segmentation['metastases'][m]['location'] = {}
p_offset = np.asarray(region['patches'][metastasis['patch_id']]['offset'])
segmentation['metastases'][m]['location']['offset'] = (p_offset + metastasis['offset']).tolist()
segmentation['metastases'][m]['location']['center'] = (p_offset + metastasis['offset'] + metastasis['CoM']).tolist()
# Add info from drug channel: are metastases significantly targeted?
# --> Yes, if mean(FG) significantly above 1.5*mean(BG)
if(dataset == 'F15'):
drugvol = filehandling.readNifti(pathDrug + 'patchvolume_' + str(metastasis['patch_id']) + '.nii')
otherblobs = dataconversions.filter_dicts(segmentation['metastases'],'patch_id',metastasis['patch_id'])
metastasis = p_leo.characterize_drugtargeting(metastasis,drugvol,min_padding=25,thr=1.5,otherblobs=otherblobs)
for m, metastasis in enumerate(segmentation['metastases']):
print('Loop 2/2: Adding information for metastasis #' + str(metastasis['global_id']))
# compute distance to nearest neighbor metastasis
segmentation['metastases'][m]['location']['distNN'] = 99999
segmentation['metastases'][m]['location']['distNNtargeted'] = 99999
segmentation['metastases'][m]['location']['distNNuntargeted'] = 99999
for neighbor in segmentation['metastases']:
dist = blobanalysis.point_dist(metastasis['location']['center'],neighbor['location']['center'])
if(metastasis['global_id'] is not neighbor['global_id']):
segmentation['metastases'][m]['location']['distNN'] = np.min([segmentation['metastases'][m]['location']['distNN'],dist])
if(dataset == 'F15'):
if(neighbor['DrugCharacterization']['ttest_result']):