<jupyter_start><jupyter_text>notebook: segmenting nuclei with the cellpose napari plugin OverviewPlugins extend the functionality of napari and can be combined together to build workflows. Many plugins exist for common analysis tasks such as segmentation and filtering. In this activity, we will segment nuclei using the [cellpose napari plugin](https://github.com/MouseLand/cellpose-napari). Please visit the [napari hub](https://www.napari-hub.org/) for a listing of the available plugins. Data sourceThe data were downloaded from the [OpticalPooledScreens github repository](https://github.com/feldman4/OpticalPooledScreens). Loading the dataWe will start by loading an image of DAPI stained nuclei. We can use `scikit-image`'s `imread()` function to download the data from the link below and load it into a numpy array called `nuclei`.<jupyter_code>from skimage import io

url = 'https://raw.githubusercontent.com/kevinyamauchi/napari-spot-detection-tutorial/main/data/nuclei_cropped.tif'
nuclei = io.imread(url)<jupyter_output><empty_output><jupyter_text>Viewing the imageAs we did in the previous notebooks, we can view the image in napari using the `napari.view_image()` function. Here we set the colormap to `magma`.<jupyter_code>import napari

viewer = napari.view_image(nuclei, colormap='magma')
from napari.utils import nbscreenshot

nbscreenshot(viewer)<jupyter_output><empty_output>
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