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%matplotlib inline
Tutorial for fusion along detection#
This tutorial shows how to apply Leonardo-Fuse to integrate light-sheet datasets with dual-sided illumination and dual-sided detection together.
The data used here is available in zenodo. We use volumes “D1_I0.tif”, “D1_I1.tif”, “D2_I0.tif” and “D2_I1.tif” for this tutorial. Here, “I0” and “I1” mean two opposite illumination lenses, while “D0” and “D1” means two opposing detection lenses. Datasets are well-aligned in advance.
Import packages & data#
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# in case Leonardo is not installed yet
!pip install leonardo_toolset
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[1]:
# 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"),
)
source_part3 = 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_part4 = 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_D1_I0 = tifffile.imread(source_part1)
X_D1_I1 = tifffile.imread(source_part2)
X_D2_I0 = tifffile.imread(source_part3)
X_D2_I1 = tifffile.imread(source_part4)
Start fusion#
First, let’s visualize the maximum intensity projection of the four datasets to figure out the orientation of the system:
[2]:
import matplotlib.pyplot as plt
import scipy
fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, dpi=150)
ax1.imshow(X_D1_I0.max(0), vmax = 2500)
ax1.set_title("MIP of X_D1_I0 in xy", fontsize=8, pad=1)
ax1.axis("off")
ax2.imshow(X_D1_I1.max(0), vmax = 2500)
ax2.set_title("MIP of X_D1_I1 in xy", fontsize=8, pad=1)
ax2.axis("off")
ax3.imshow(X_D2_I0.max(0), vmax = 2500)
ax3.set_title("MIP of X_D2_I0 in xy", fontsize=8, pad=1)
ax3.axis("off")
ax4.imshow(X_D2_I1.max(0), vmax = 2500)
ax4.set_title("MIP of X_D2_I1 in xy", fontsize=8, pad=1)
ax4.axis("off")
plt.show()
fig, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, dpi=150)
ax1.imshow(scipy.ndimage.zoom(X_D1_I0.max(1), (2.5, 0.65)), vmax = 2500)
ax1.set_title("MIP of X_D1_I0 in xz", fontsize=8, pad=1)
ax1.axis("off")
ax2.imshow(scipy.ndimage.zoom(X_D1_I1.max(1), (2.5, 0.65)), vmax = 2500)
ax2.set_title("MIP of X_D1_I1 in xz", fontsize=8, pad=1)
ax2.axis("off")
ax3.imshow(scipy.ndimage.zoom(X_D2_I0.max(1), (2.5, 0.65)), vmax = 2500)
ax3.set_title("MIP of X_D2_I0 in xz", fontsize=8, pad=1)
ax3.axis("off")
ax4.imshow(scipy.ndimage.zoom(X_D2_I1.max(1), (2.5, 0.65)), vmax = 2500)
ax4.set_title("MIP of X_D2_I1 in xz", fontsize=8, pad=1)
ax4.axis("off")
plt.show()
_9_0.png)
_9_1.png)
We can observe that:
the four datasets stand for:
X_D1_I0: illuminated from the top (in the image space) + recorded using ventral camera;
X_D1_I1: illuminated from the bottom (in the image space) + recorded using ventral camera;
X_D2_I0: illuminated from the bottom (in the image space) + recorded using dorsal camera;
X_D2_I1: illuminated from the top (in the image space) + recorded using dorsal camera;
two datasets recorded using dorsal camera, i.e., “D2”, need to be flipped vertically to be mapped into the same space as datasets recorded via ventral camera.
Next, we can initialize a FUSE_det object. Here, we initilize FUSE_det using default parameters. A more advanced list of available arguments can be found in section API reference
[3]:
from leonardo_toolset import FUSE_det
model = FUSE_det()
[Leonardo-Fuse] Backend: PyTorch | Device: cuda
[Leonardo-Fuse] Backend: PyTorch | Device: cuda
[Leonardo-Fuse] Backend: PyTorch | Device: cuda
Finally, we start fusion by calling train function. Four datasets are feded as top_illu_ventral_det_data, bottom_illu_ventral_det_data, top_illu_dorsal_det_data and bottom_illu_dorsal_det_data, respectively, corresponding to their illumination orientation and camera position. Since the datasets are well aligned alread, registration is not needed. Additionally, since we observed flipping vertically in the image space is required for stacks with dorsal camera in advance,
require_flipping_along_illu_for_dorsaldet need to be True. The fusion result, together with a few intermidiate results, will be saved in folder save_folder under path save_path.
[4]:
# start fusion
save_path = "./"
save_folder = "fuse_det_result"
out = model.train(require_registration = False,
require_flipping_along_illu_for_dorsaldet = True,
require_flipping_along_det_for_dorsaldet = False,
top_illu_ventral_det_data = X_D1_I0,
bottom_illu_ventral_det_data = X_D1_I1,
top_illu_dorsal_det_data = X_D2_I1,
bottom_illu_dorsal_det_data = X_D2_I0,
save_path = save_path,
save_folder = save_folder,
)
Cannot skip dual-illu fusion for ventral det...
Cannot skip dual-illu fusion for dorsal det...
Fusion along illumination for ventral camera...
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, 24.59it/s]
Segment sample...
watershed (125): 100%|██████████| 250/250 [00:09<00:00, 25.18it/s]
refine pair-wise segmentation result: 100%|██████████| 250/250 [00:06<00:00, 39.98it/s]
refine along z: 100%|██████████| 2704/2704 [00:02<00:00, 1128.37it/s]
Dual-illumination fusion...
to GPU...
No.049 iteration EM: maximum changes = 1.0
Stitching...
fusion: 100%|██████████| 250/250 [00:14<00:00, 17.68it/s]
_13_8.png)
Save...
Fusion along illumination for dorsal camera...
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
_13_10.png)
Calculate volumetric measurements...
top/left view: minimum intensity = 56.0, maximum intensity = 2865.0, OTSU threshold = 148.0
bottom/right view: minimum intensity = 55.0, maximum intensity = 4530.0, OTSU threshold = 151.0
Extract features...
NSCT: 100%|██████████| 250/250 [00:09<00:00, 26.32it/s]
Segment sample...
watershed (77): 100%|██████████| 250/250 [00:11<00:00, 22.23it/s]
refine pair-wise segmentation result: 100%|██████████| 250/250 [00:06<00:00, 36.49it/s]
refine along z: 100%|██████████| 2704/2704 [00:03<00:00, 750.24it/s]
Dual-illumination fusion...
to GPU...
No.049 iteration EM: maximum changes = 1.0
Stitching...
fusion: 100%|██████████| 250/250 [00:14<00:00, 17.50it/s]
_13_17.png)
Save...
Localize sample...
read in...
startX endX startY endY maxv
ventral 0 1024 0 1024 5689.999023
dorsal 0 1024 0 1024 5689.999023
in summary 0.0 1024.0 0.0 1024.0 5689.999023
_13_19.png)
watershed: 100%|██████████| 250/250 [00:06<00:00, 37.63it/s]
For top/left Illu...
read in...
NSCT: 100%|██████████| 250/250 [00:09<00:00, 26.33it/s]
to GPU...
No.049 iteration EM: maximum changes = 1.0
For bottom/right Illu...
read in...
NSCT: 100%|██████████| 250/250 [00:09<00:00, 25.47it/s]
to GPU...
No.049 iteration EM: maximum changes = 1.0
Stitching...
read in...
intergrate fusion decision: 100%|██████████| 250/250 [00:15<00:00, 16.01it/s]
refine along z: 100%|██████████| 4761/4761 [00:04<00:00, 1057.26it/s]
fusion: 100%|██████████| 250/250 [00:50<00:00, 4.96it/s]
_13_27.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
├─ X_D1_I0.tif
├─ X_D1_I1.tif
├─ X_D2_I1.tif
├─ X_D2_I0.tif
and call the train function as follows:
out = model.train(data_path = data_path,
require_registration = False,
require_flipping_along_illu_for_dorsaldet = True,
require_flipping_along_det_for_dorsaldet = False,
top_illu_ventral_det_data = "X_D1_I0.tif",
bottom_illu_ventral_det_data = "X_D1_I1.tif",
top_illu_dorsal_det_data = "X_D2_I1.tif",
bottom_illu_dorsal_det_data = "X_D2_I0.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_ventral_det_data, right_illu_ventral_det_data, left_illu_dorsal_det_data and right_illu_dorsal_det_data:
out = model.train(require_registration = False,
require_flipping_along_illu_for_dorsaldet = True,
require_flipping_along_det_for_dorsaldet = False,
left_illu_ventral_det_data = X_D1_I0, # suppose X_D1_I0 is illuminated from the left
right_illu_ventral_det_data = X_D1_I1, # suppose X_D1_I1 is illuminated from the right
left_illu_dorsal_det_data = X_D2_I1, # suppose X_D2_I1 is illuminated from the left
right_illu_dorsal_det_data = X_D2_I0, # suppose X_D2_I0 is illuminated from the right
save_path = save_path,
save_folder = save_folder,
)
Note 3: intergrate with registration workflow#
In reality, the dorsal camera can be mimicked by rotating the specimen 180 degree, which eases sample mounting. Nevertheless, it requires the registration of the volumes imaged after rotation to those captured before rotation. In princple, we recommend that users of Leonardo-Fuse manually register the input stacks before-hand. Still, we offer automatic registration in FUSE_det, which can be enabled as following:
out = model.train(require_registration = True,
require_flipping_along_det_for_dorsaldet = True,
z_spacing = ...,
xy_spacing = ...,
...
)
where require_flipping_along_det_for_dorsaldet is used in case the two datasets recorded after rotation is flipped along z, z_spacing and xy_spacing are axial and lateral resolution to be used for registration.
Note 4: fuse dual-side detection only#
Sometimes, in some special light-sheet based systems, for example Balze, the dual-sided illumination is fused on-the-fly. Thus, FUSE_det is only to fuse two datasets with opposing detecting orientations:
out = model.train(ventral_det_data = ...,
dorsal_det_data = ...,
left_right = ...,
...
)
where ventral_det_data and dorsal_det_data are arguments for input datsets recorded using ventral camera or dorsal camera, respectively (could also be before/after rotation). left_right is a boolean arguments to say whether, in the image space, the illumination orientation is from left and right, or top and bottom.
Note 5: fuse extremely large datasets#
Since light-sheet microscopy is especially helpful to image extremely large specimen, Leonardo offers solution to fuse extremely huge datasets. The key idea is to train the fusion algorithm in a downsampled space, and then apply it to the datasets with full resolution:
out = model.train(ventral_det_data = ...,
dorsal_det_data = ...,
left_right = ...,
xy_downsample_ratio = .., # the downsampling ratio along xy to trainthe low-resolution fusion model
z_downsample_ratio = .., # the downsampling ratio along z to trainthe low-resolution fusion model
)
for now, it only supports to fuse extremely large datasets acquired using Balze system, i.e., with illumination fused on-the-fly.