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%matplotlib inline
Tutorial for Leonardo as a complete pipeline#
This tutorial shows how to apply Leonardo to fuse datasets, specifically with dual-sided illumination, and remove stripe artifacts with optimal processing time.
The data used here is available in zenodo. We use volumes “D2_I0.tif” and “D2_I1.tif” for this tutorial. Here, “I0” and “I1” mean two opposite illumination lenses.
As a normal workflow, stripe removal can be performed on each individual input stack separately, followed by image fusion. However, Leonardo-DeStripes requires much longer processing time than Leonaro-Fuse in practice. Hence, we here propose Leonardo-DeStripe-Fuse, which applies fusion before stripe removal without copromising the performance.
Import packages & data#
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# in case Leonardo is not installed yet
!pip install leonardo_toolset
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[2]:
# load sample dataset
import pooch
from pathlib import Path
import tifffile
data_path = "./"
source_part1 = pooch.retrieve(
url="https://zenodo.org/records/14215090/files/D2_I0.tif",
known_hash="md5:83ad8c084f3e8f6d5753ee05a4378bdd",
fname="D2_I0.tif",
path=data_path / Path("download"),
)
source_part2 = pooch.retrieve(
url="https://zenodo.org/records/14215090/files/D2_I1.tif",
known_hash="md5:859a680fad7f86b61636a04d1971e787",
fname="D2_I1.tif",
path=data_path / Path("download"),
)
X_D2_I0 = tifffile.imread(source_part1)
X_D2_I1 = tifffile.imread(source_part2)
Downloading data from 'https://zenodo.org/records/14215090/files/D2_I0.tif' to file '/content/download/D2_I0.tif'.
Downloading data from 'https://zenodo.org/records/14215090/files/D2_I1.tif' to file '/content/download/D2_I1.tif'.
Start Leonardo-DeStripe-Fuse#
Step 1: Leonardo-Fuse#
Same as normal use of Leonardo-Fuse. The only difference is to turn save_separate_results to be True, to save some intermediate results useful for the later DeStripe:
[3]:
from leonardo_toolset import FUSE_illu
save_path = "./"
save_folder = "fuse_illu_result"
model = FUSE_illu()
out = model.train(top_illu_data = X_D2_I1,
bottom_illu_data = X_D2_I0,
save_path = save_path,
save_folder = save_folder,
save_separate_results = True,
)
[Leonardo-Fuse] Backend: PyTorch | Device: cuda
Read in...
Localize sample...
startX endX startY endY maxv
top 0 1024 0 1024 3473.999756
bottom 0 1024 0 1024 5689.999023
in summary 0.0 1024.0 0.0 1024.0 5689.999023
Calculate volumetric measurements...
top/left view: minimum intensity = 60.0, maximum intensity = 2189.0, OTSU threshold = 148.0
bottom/right view: minimum intensity = 53.0, maximum intensity = 4757.0, OTSU threshold = 151.0
Extract features...
NSCT: 100%|██████████| 250/250 [00:10<00:00, 23.91it/s]
Segment sample...
watershed (23): 100%|██████████| 250/250 [00:13<00:00, 18.89it/s]
refine pair-wise segmentation result: 100%|██████████| 250/250 [00:05<00:00, 41.88it/s]
refine along z: 100%|██████████| 2704/2704 [00:02<00:00, 1280.73it/s]
Dual-illumination fusion...
to GPU...
No.049 iteration EM: maximum changes = 1.0
Stitching...
fusion: 100%|██████████| 250/250 [00:47<00:00, 5.24it/s]
Save...
Step 2: Leonardo-DeStripe
Initialize DeStripe in the same way, but when training the model, input the corresponding two slices for X_D2_I1 and X_D2_I0, respectively, instead of the fusion result, togetheer with the corresponding fusion_illu_mask saved under save_path/save_folder/top_illu/fuse_illu_mask. Here, we only use slice 206 for demostration, but of course can also do the same thing for the whole stack:
[4]:
from leonardo_toolset import DeStripe
import numpy as np
import matplotlib.pyplot as plt
model = DeStripe()
recon = model.train(is_vertical = True,
display = True,
angle_offset_0 = [0],
angle_offset_1 = [-3],
x_0 = X_D2_I1[206],
x_1 = X_D2_I0[206],
fusion_mask = np.load("/content/fuse_illu_result/top_illu/fuse_illu_mask/00206.npz")["mask"],
)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, dpi=300)
ax1.imshow(X_D2_I1[206], vmax = 500)
ax1.set_title("Input1", fontsize=8, pad=1)
ax1.axis("off")
ax2.imshow(X_D2_I0[206], vmax = 500)
ax2.set_title("Input2", fontsize=8, pad=1)
ax2.axis("off")
ax3.imshow(recon[0], vmax = 500)
ax3.set_title("Output", fontsize=8, pad=1)
ax3.axis("off")
[Leonardo-DeStripe] Backend: JAX | Device: gpu
Start DeStripe-Fuse...
warning: illumination orientation is not given. post-processing will be ignored.
Done
[4]:
(np.float64(-0.5), np.float64(1023.5), np.float64(1023.5), np.float64(-0.5))
In summary, use x_0, x_1, instead of x for feeding the data-to-be-processed. Correspondinly, use angle_offset_0 and angle_offset_1 for indicating stripe orientations. Make sure that x_0 and x_1 shoud be consistent with fusion_mask. In this case, x_0 should be the dataset with illumiation from the top, whereas x_1 is the stack with illumnation from the bottom.
As discussed under Leonardo-DeStripe section, post-processing for sample structure preservation is also available for Leonardo-DeStripe-Fuse, by simply using illu_orient_0, illu_orient_1…corresponding to x_0, x_1 and so on:
[5]:
from leonardo_toolset import DeStripe
import numpy as np
import matplotlib.pyplot as plt
model = DeStripe()
recon = model.train(is_vertical = True,
display = True,
angle_offset_0 = [0],
angle_offset_1 = [-3],
x_0 = X_D2_I1[206],
x_1 = X_D2_I0[206],
illu_orient_0 = "top",
illu_orient_1 = "bottom",
fusion_mask = np.load("/content/fuse_illu_result/top_illu/fuse_illu_mask/00206.npz")["mask"],
)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, dpi=300)
ax1.imshow(X_D2_I1[206], vmax = 500)
ax1.set_title("Input1", fontsize=8, pad=1)
ax1.axis("off")
ax2.imshow(X_D2_I0[206], vmax = 500)
ax2.set_title("Input2", fontsize=8, pad=1)
ax2.axis("off")
ax3.imshow(recon[0], vmax = 500)
ax3.set_title("Output", fontsize=8, pad=1)
ax3.axis("off")
[Leonardo-DeStripe] Backend: JAX | Device: gpu
Start DeStripe-Fuse...
Done
[5]:
(np.float64(-0.5), np.float64(1023.5), np.float64(1023.5), np.float64(-0.5))
