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%matplotlib inline
Tutorial for fusion along illumination#
This tutorial shows how to apply Leonardo-Fuse to integrate light-sheet datasets with dual-sided illumination together.
The data used here is available in zenodo. We use volumes “D1_I0.tif” and “D1_I1.tif” for this tutorial. Here, “I0” and “I1” mean two opposite illumination lenses, while “D1” means the same detection lens.
Import packages & data#
[1]:
# in case Leonardo is not installed yet
!pip install leonardo_toolset
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[3]:
# load sample dataset
import pooch
from pathlib import Path
import tifffile
data_path = "./"
source_part1 = pooch.retrieve(
url="https://zenodo.org/records/14215090/files/D1_I0.tif",
known_hash="md5:ef10aa423ffc5175cd44284206794ec5",
fname="D1_I0.tif",
path=data_path / Path("download"),
)
source_part2 = pooch.retrieve(
url="https://zenodo.org/records/14215090/files/D1_I1.tif",
known_hash="md5:b3d68aea26ce0b1e26324f2f588207e0",
fname="D1_I1.tif",
path=data_path / Path("download"),
)
X0 = tifffile.imread(source_part1)
X1 = tifffile.imread(source_part2)
Start fusion#
First, let’s visualize the maximum intensity projection of the two datasets to figure out the orientation of the system:
[4]:
import matplotlib.pyplot as plt
fig, (ax1, ax2) = plt.subplots(1, 2, dpi=150)
ax1.imshow(X0.max(0))
ax1.set_title("MIP of X0", fontsize=8, pad=1)
ax1.axis("off")
ax2.imshow(X1.max(0))
ax2.set_title("MIP of X1", fontsize=8, pad=1)
ax2.axis("off")
plt.show()
_9_0.png)
We can observe that light sheet comes from top and bottom (in the image space) for volume X0 and X1, respectively. Next, we can initialize a FUSE_illu object. Here, we initilize FUSE_illu using default parameters. A more advanced list of available arguments can be found in section API reference
[5]:
from leonardo_toolset import FUSE_illu
model = FUSE_illu()
[Leonardo-Fuse] Backend: PyTorch | Device: cuda
Finally, we start fusion by calling train function. datasets X0 and X1 are fed as top_illu_data and bottom_illu_data, respectively, corresponding to their illumination orientation. The fusion result, together with a few intermidiate results, will be saved in folder save_folder under path save_path.
[6]:
# start fusion
save_path = "./"
save_folder = "fuse_illu_result"
out = model.train(top_illu_data = X0,
bottom_illu_data = X1,
save_path = save_path,
save_folder = save_folder,
)
Read in...
Localize sample...
startX endX startY endY maxv
top 0 1024 0 1024 3454.000244
bottom 0 1024 0 1024 2523.999512
in summary 0.0 1024.0 0.0 1024.0 3454.000244
_13_1.png)
Calculate volumetric measurements...
top/left view: minimum intensity = 57.0, maximum intensity = 2537.0, OTSU threshold = 149.0
bottom/right view: minimum intensity = 61.0, maximum intensity = 2049.0, OTSU threshold = 144.0
Extract features...
NSCT: 100%|██████████| 250/250 [00:10<00:00, 24.68it/s]
Segment sample...
watershed (125): 100%|██████████| 250/250 [00:10<00:00, 24.13it/s]
refine pair-wise segmentation result: 100%|██████████| 250/250 [00:05<00:00, 45.62it/s]
refine along z: 100%|██████████| 2704/2704 [00:02<00:00, 933.75it/s]
Dual-illumination fusion...
to GPU...
No.049 iteration EM: maximum changes = 1.0
Stitching...
fusion: 100%|██████████| 250/250 [00:13<00:00, 18.78it/s]
_13_8.png)
Save...
Note 1: inputs with file names provided#
In reality, to facilitate batch processing, users can also feed in data-to-be-fused without reading them into the memory. To do so, please save the data-to-be-fused in such structure:
data_path
├─ I0_D1.tif
├─ I1_D1.tif
and call the train function as follows:
out = model.train(data_path = data_path,
top_illu_data = "I0_D1.tif",
bottom_illu_data = "I1_D1.tif",
save_path = save_path,
save_folder = save_folder,
)
Note 2: fuse datasets with left/right illumination orientations#
Depends on the system, the illumination orientation in then image space can also from left or right. In this way, input datasets should be called by left_illu_data and right_illu_data, instead of top_illu_data and bottom_illu_data:
out = model.train(left_illu_data = X0, # suppose X0 is illuminated from the left
right_illu_data = X1, # suppose X0 is illuminated from the right
save_path = save_path,
save_folder = save_folder,
)