def load_bite_image(well, fview=1):
well_imgs = [f for f in fnames if well in f and 'ch2' not in f]
wellpos = [f for f in well_imgs if 'f0' + str(fview) + 'p' in f]
imgseries = load_image_series(path=platedir, imgfiles=wellpos)
imgseries = imgseries.reshape((8, 4, 2160, 2160))
mipseries = np.amax(imgseries, axis=0)
return mipseries
outdir = 'figures/segfree/organoids'
if not os.path.exists(outdir):
os.makedirs(outdir)
random.seed(1108)
imglist = []
titles = []
for path in paths:
fnames, all_wells = get_all_wells(path=path)
# randomly sample 10 wells
sel_wells = random.sample(all_wells, k=5)
for w in sel_wells:
print("Processing well: %s" % w)
well_files = [f for f in fnames if w in f]
imgstack = load_image_series(
path=path, imgfiles=[w for w in well_files if 'P00001' in w])
imgstack = imgstack.swapaxes(0, -1)
imglist.append(imgstack)
titles.append(re.search('(.+)(--W[0-9]+)', well_files[0]).group(1))
# 3D profiling
segf = SegfreeProfiler(tile_size=(20, 20),
n_block_types=20,
n_supblock_types=50)
org_prof = segf.fit_transform(imglist)
pickle.dump(segf, open("segf_organoids.pkl", "wb"))
plt.plot(np.cumsum(segf.pca.explained_variance_ratio_), linewidth=3)
sn.despine()
plt.axhline(y=1, color='black', linestyle=':')
plt.xlabel('Number of principal components')
well = all_wells[wellnum]
fnames = [f for f in os.listdir(imgdir) if '.tiff' in f]
# load minimum lysosomal intensity (estmated background)
thresh_df = pd.read_csv('data/coculture_metafiles/thresholds/' + plate +
'.csv')
bg_thresh = np.min(thresh_df[['thresh_mono', 'thresh_co']].values)
# apply gamma correction
gamma = 0.3
imgdata = []
for fview in range(1, 4):
wellpos = well + 'f' + str(fview).zfill(2)
wfiles = [f for f in fnames if wellpos in f and '(2)' not in f]
imgstack = load_image_series(
path=imgdir, imgfiles=[w for w in wfiles if 'ch1' in w])
hoechst = np.max(imgstack, axis=0)
hoechst = hoechst**gamma
imgstack = load_image_series(
path=imgdir, imgfiles=[w for w in wfiles if 'ch2' in w])
ly = np.max(imgstack, axis=0)
ly = ly**gamma
well_df = eval_df[eval_df['wellpos'] == wellpos]
if well_df.shape[0]:
rmax, cmax = hoechst.shape
bbox = read_bbox(df=well_df,
rmax=rmax,
cmax=cmax,
columns=['ymin', 'xmin', 'ymax', 'xmax'],
well_id = int(sys.argv[3])-1
fnames = [f for f in os.listdir(platedir) if '.tiff' in f]
all_wells = list(set([re.search('r[0-9]+c[0-9]+',f).group(0) for f in fnames]))
all_wells.sort()
well = all_wells[well_id]
well_imgs = [f for f in fnames if well in f and 'ch2' not in f]
gamma = 0.5
pad = 5
imgdata = []
for i in range(1,5):
wellpos = [f for f in well_imgs if 'f0' + str(i)+ 'p' in f]
imgseries = load_image_series(path=platedir, imgfiles=wellpos)
imgseries = imgseries.reshape((8, 4, 2160,2160))
mipseries = np.amax(imgseries, axis=0)
hoechst = mipseries[0]
img_th = threshold_img(hoechst**gamma, method='otsu', binary=False)
img_s = shape_index(img_th)
img_enh = nantonum(img_s, pad=-1)
# run blob detection on the shape-index enhanced image
blobs = blob_log(img_enh,
min_sigma=10,
max_sigma=14,
threshold=0.05)
if len(blobs):
bbox = np.stack([np.array([bl[1] - bl[2] - pad,
bl[1] + bl[2] + pad,