4. Interactive Analysis and Next Steps

Goal: Run a complete filter → threshold → segment → measure workflow using a plugin GUI, take a brief look at the napari console, and learn where to go from here.

napari-skimage Segmentation Demo (5 min + 20 min hands-on)¶

napari-skimage provides GUI access to common image-processing steps powered by scikit-image — no code required.

Install it:

Plugins > Install/Uninstall Plugins… → search napari-skimage → Install
Restart napari

Workflow¶

Use File > Open Sample > napari builtins > Cells (3D + 2Ch) as your starting image.

Make sure you look through all your z-slices when gauging whether a particular filter or thresholding algorithm worked as expected!

Step	What to do	Why
1. Load	Open Cells (3D + 2Ch)	Your raw data
2. Filter	Layers > Filter > Filtering > Gaussian filter — apply to `nuclei`	Reduce noise
3. Threshold	Layers > Filter > Thresholding > Automated Threshold — apply Otsu to the filtered layer. The threshold output is a binary Labels mask.	Separate foreground from background
4. Label	Layers > Segment > Label connected components on the mask to generate a new Labels layer with a unique ID (and color) for each object.	Each connected region gets a unique integer ID
5. Measure	Layers > Measure > Regionprops (labels) — select the Labels and nuclei layers	Outputs a table of features e.g. area, intensity, eccentricity per cell
6. Save	Click Save Results in the Regionprops widget	Export measurements for further analysis

import napari
import numpy as np

from scipy import ndimage as ndi
from skimage import data, filters, morphology
from napari.utils import nbscreenshot


cells3d = data.cells3d()
viewer = napari.Viewer()
membranes_layer, nuclei_layer = viewer.add_image(
    cells3d, channel_axis=1, name=['membranes', 'nuclei']
)
membrane, nuclei = cells3d.transpose((1, 0, 2, 3)) / np.max(cells3d)
edges = filters.scharr(nuclei)
denoised = ndi.median_filter(nuclei, size=3)
thresholded = denoised > filters.threshold_li(denoised)
cleaned = morphology.remove_small_objects(
    morphology.remove_small_holes(thresholded, 20**3),
    20**3,
)

segmented = ndi.label(cleaned)[0]

labels_layer = viewer.add_labels(segmented)

# viewer.dims.current_step = (27, 0, 0)
viewer.dims.ndisplay=3
viewer.camera.angles = (-20, 35, -40)

/home/runner/work/workshops/workshops/.pixi/envs/default/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

/tmp/ipykernel_4109/739284975.py:19: FutureWarning: Parameter `area_threshold` is deprecated since version 0.26.0 and will be removed in 2.0.0 (or later). To avoid this warning, please use the parameter `max_size` instead. For more details, see the documentation of `remove_small_holes`. Note that the new threshold removes objects smaller than **or equal to** its value, while the previous parameter only removed smaller ones.
  morphology.remove_small_holes(thresholded, 20**3),
/tmp/ipykernel_4109/739284975.py:18: FutureWarning: Parameter `min_size` is deprecated since version 0.26.0 and will be removed in 2.0.0 (or later). To avoid this warning, please use the parameter `max_size` instead. For more details, see the documentation of `remove_small_objects`. Note that the new threshold removes objects smaller than **or equal to** its value, while the previous parameter only removed smaller ones.
  cleaned = morphology.remove_small_objects(

nbscreenshot(viewer)

How many nuclei did your segmentation find? (check the Zulip stream for screenshots!)
What measurements were in the regionprops table?
Open questions about the workflow or napari in general?

Where to Go From Here (5 min)¶

Learning Resources¶

Resource	Link
napari documentation	napari.org/stable
Gallery	napari.org/stable/gallery
Plugin search	napari-hub.org

Community & Getting Help¶

Channel	Description
Zulip chat	Chat with the napari community - developers, users and contributors. Ask questions, share your analyses, or just lurk for info!
napari GitHub	Participate in development, report bugs and request new features.
Image.sc	Ask questions about using napari, how to perform different image analysis tasks, and get help from experienced image analysts.

Next Steps¶

Try opening one of your own images in napari
Browse napari-hub.org for plugins relevant to your field
Add a new example or sample data to napari!

Survey + Wrap-up (10 min)¶

Please fill in the post-workshop survey (link shared by instructors).

Your feedback helps improve these materials and future workshops — thank you for participating!