3D细胞参数化

基于球谐函数系数的3D细胞质和核质的参数化

安装

稳定版本： pip install aicscytoparam
开发头： pip install git+https://github.com/AllenCell/aics-cytoparam.git

如何使用

这里概述了如何使用aicscytoparam创建3D细胞的参数化示例。在这种情况下，3D细胞将由细胞分割、核分割和表示标记蛋白荧光信号的荧光蛋白（FP）图像表示。

# Import required packages
import numpy as np
import matplotlib.pyplot as plt
from aicscytoparam import cytoparam
from skimage import morphology as skmorpho

# First create a cuboid cell with an off-center cuboid nucleus
# and get the spherical harmonics coefficients of this cell and nucleus:
w = 100
mem = np.zeros((w, w, w), dtype = np.uint8)
mem[20:80, 20:80, 20:80] = 1
nuc = np.zeros((w, w, w), dtype = np.uint8)
nuc[40:60, 40:60, 30:50] = 1

# Create an FP signal located in the top half of the cell and outside the
# nucleus:
gfp = np.random.rand(w**3).reshape(w,w,w)
gfp[mem==0] = 0
gfp[:, w//2:] = 0
gfp[nuc>0] = 0

# Vizualize a center xy cross-section of our cell:
plt.imshow((mem + nuc)[w//2], cmap='gray')
plt.imshow(gfp[w // 2], cmap='gray', alpha=0.25)
plt.axis('off')

# Use aicsshparam to expand both cell and nuclear shapes in terms of spherical
# harmonics:
coords, coeffs_centroid = cytoparam.parameterize_image_coordinates(
    seg_mem=mem,
    seg_nuc=nuc,
    lmax=16, # Degree of the spherical harmonics expansion
    nisos=[32, 32] # Number of interpolation layers
)
coeffs_mem, centroid_mem, coeffs_nuc, centroid_nuc = coeffs_centroid

# Run the cellular mapping to create a parameterized intensity representation
# for the FP image:
gfp_representation = cytoparam.cellular_mapping(
    coeffs_mem=coeffs_mem,
    centroid_mem=centroid_mem,
    coeffs_nuc=coeffs_nuc,
    centroid_nuc=centroid_nuc,
    nisos=[32, 32],
    images_to_probe=[('gfp', gfp)]
).data.squeeze()

# The FP image is now encoded into a representation of its shape:
print(gfp_representation.shape)

(65, 8194)

# Now we want to morph the FP image into a round cell.
# First we create the round cell:

from skimage import morphology as skmorpho
mem_round = skmorpho.ball(w // 3) # radius of our round cell
nuc_round = skmorpho.ball( w// 3) # radius of our round nucleus
# Erode the nucleus so it becomes smaller than the cell
nuc_round = skmorpho.binary_erosion(
    nuc_round, selem=np.ones((20, 20, 20))
    ).astype(np.uint8)

# Vizualize a center xy cross-section of our round cell:
plt.imshow((mem_round + nuc_round)[w // 3], cmap='gray')
plt.axis('off')

# Next we need to parameterize the coordinates of our round
# cell:
coords_round, _ = cytoparam.parameterize_image_coordinates(
    seg_mem=mem_round,
    seg_nuc=nuc_round,
    lmax=16,
    nisos=[32, 32]
)

# Now we are ready to morph the FP image into our round cell:
gfp_morphed = cytoparam.morph_representation_on_shape(
    img=mem_round + nuc_round,
    param_img_coords=coords_round,
    representation=gfp_representation
)
# Visualize the morphed FP image:
plt.imshow((mem_round + nuc_round)[w // 3], cmap='gray')
plt.imshow(gfp_morphed[w // 3], cmap='gray', alpha=0.25)
plt.axis('off')

参考文献

有关如何使用此包分析艾伦细胞研究所超过200k个单细胞图像数据集的示例，请参阅我们的bioaRxiv论文。

开发

有关开发代码的信息，请参阅CONTRIBUTING.md。

有问题吗？

如果您有任何问题，请自由地在我们艾伦细胞论坛上留下评论： https://forum.allencell.org/。

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