Settings¶
The important settings are described on this page. See the Cellpose class for all run options.
Here is an example of calling the Cellpose class and running a list of images for reference:
from cellpose import models
import skimage.io
# model_type='cyto' or model_type='nuclei'
model = models.Cellpose(gpu=False, model_type='cyto')
files = ['img0.tif', 'img1.tif']
imgs = [skimage.io.imread(f) for f in files]
masks, flows, styles, diams = model.eval(imgs, diameter=None, channels=[0,0],
threshold=0.4, do_3D=False)
You can make lists of channels/diameter for each image, or set the same channels/diameter for all images as shown in the example above.
Diameter¶
The cellpose models have been trained on images which were rescaled to all have the same diameter (30 pixels in the case of the cyto model and 17 pixels in the case of the nuclei model). Therefore, cellpose needs a user-defined cell diameter (in pixels) as input, or to estimate the object size of an image-by-image basis.
The automated estimation of the diameter is a two-step process using the style vector from the network, a 64-dimensional summary of the input image. We trained a linear regression model to predict the size of objects from these style vectors on the training data. On a new image the procedure is as follows.
Run the image through the cellpose network and obtain the style vector. Predict the size using the linear regression model from the style vector.
Resize the image based on the predicted size and run cellpose again, and produce masks. Take the final estimated size as the median diameter of the predicted masks.
For automated estimation set diameter = None.
However, if this estimate is incorrect please set the diameter by hand.
Changing the diameter will change the results that the algorithm outputs. When the diameter is set smaller than the true size then cellpose may over-split cells. Similarly, if the diameter is set too big then cellpose may over-merge cells.
Channels¶
Cytoplasm model (‘cyto’)¶
The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel. Here are the options for each: 1. 0=grayscale, 1=red, 2=green, 3=blue 2. 0=None (will set to zero), 1=red, 2=green, 3=blue
Set channels to a list with each of these elements, e.g.
channels = [0,0] if you want to segment cells in grayscale or for single channel images, or
channels = [2,3] if you green cells with blue nuclei.
Nucleus model (‘nuclei’)¶
The nuclear model in cellpose is trained on two-channel images, where
the first channel is the channel to segment, and the second channel is
always set to an array of zeros. Therefore set the first channel as
0=grayscale, 1=red, 2=green, 3=blue; and set the second channel to zero, e.g.
channels = [0,0] if you want to segment nuclei in grayscale or for single channel images, or
channels = [3,0] if you want to segment blue nuclei.
Flow threshold¶
Note there is nothing keeping the neural network from predicting horizontal and vertical flows that do not correspond to any real shapes at all. In practice, most predicted flows are consistent with real shapes, because the network was only trained on image flows that are consistent with real shapes, but sometimes when the network is uncertain it may output inconsistent flows. To check that the recovered shapes after the flow dynamics step are consistent with real masks, we recompute the flow gradients for these putative predicted masks, and compute the mean squared error between them and the flows predicted by the network.
The flow_threshold parameter is the maximum allowed error of the flows
for each mask. The default is flow_threshold=0.4. Increase this threshold
if cellpose is not returning as many masks as you’d expect.
Similarly, decrease this threshold if cellpose is returning too many
ill-shaped masks.
Cell probability threshold¶
The network predicts 3 outputs: flows in X, flows in Y, and cell “probability”.
The predictions the network makes of the probability are the inputs to a sigmoid
centered at zero (1 / (1 + e^-x)),
so they vary from around -6 to +6. The pixels greater than the
cellprob_threshold are used to run dynamics and determine masks. The default
is cellprob_threshold=0.0. Decrease this threshold if cellpose is not returning
as many masks as you’d expect. Similarly, increase this threshold if cellpose is
returning too masks particularly from dim areas.