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Imaging Software for microscopy

Figure 1

A mosaic of screenshots of some of Napari's included sample data

Getting Started With Napari

Figure 1

A screenshot of the default Napari user interface

Figure 2

A screenshot of a fluorescence microscopy image of some cells in Napari

Figure 3

A screenshot of Napari with the main user interface sections labelled

Figure 4

Three screenshots of the cells image in napari, at different z depths

Figure 5

Closeup of Napari's dimension slider with labels sand More sliders can appear if our image has more dimensions (e.g. time series, or further channels).

Figure 6

Console A screenshot of Napari's console button

Figure 7

2D/3D A screenshot of Napari's 2D button / A screenshot of Napari's 3D button

Figure 8

A screenshot of 3D cells in Napari

Figure 9

Roll dimensions A screenshot of Napari's roll dimensions button

Figure 10

Three screenshots of the cells image in napari, with different axes being visualised

Figure 11

Transpose dimensions A screenshot of Napari's transpose dimensions button

Figure 12

Two screenshots of the cells image in napari, with dimensions swapped

Figure 13

Grid A screenshot of Napari's grid button

Figure 14

Home A screenshot of Napari's home button

Figure 15

A screenshot of Napari's layer list, showing two image layers named 'nuclei' and 'membrane'

Figure 16

A screenshot of Napari with the nuclei and membrane layer swapped

Figure 17

Cells image with blue nuclei and bright red membranes

Figure 18

Points A screenshot of Napari's point layer button

Figure 19

Shapes A screenshot of Napari's shape layer button

Figure 20

Labels A screenshot of Napari's labels layer button

Figure 21

Remove layer A screenshot of Napari's delete layer button

Figure 22

Cells image with points marking multiple nuclei

Figure 23

  • Click the ‘add points’ button Screenshot of Napari's add points button

    • Figure 24

  • Click the ‘select points’ button Screenshot of Napari's select points button

    • Figure 25

    Screenshot of searching 'matplotlib' on napari hub

    Figure 26

    Screenshot of plugin installation window in Napari

    What is an image?

    Figure 1

    Press the remove layer button A screenshot of Napari's delete layer button

    Figure 2

    A screenshot of a 2D image of human cells undergoing mitosis in Napari

    Figure 3

    A screenshot of Napari - with the mouse cursor hovering over a pixel and highlighting the corresponding pixel value

    Figure 4

    First, open Napari’s built-in Python console by pressing the console button A screenshot of Napari's console button. Note this can take a few seconds to open, so give it some time:

    Figure 5

    A screenshot of Napari's console

    Figure 6

    Note that you can also pop the console out into its own window by clicking the small A screenshot of Napari's float panel button icon on the left side.

    Figure 7

    A diagram comparing the array of numbers and image display for a simplified image of an arrow

    Figure 8

    A screenshot of Napari - with the mouse cursor hovering over a pixel and highlighting the corresponding coordinates

    Figure 9

    Diagram comparing a standard graph coordinate system (left) and the image coordinate system (right)

    Figure 10

    A diagram showing how pixel coordinates change over a simple 4x4 image

    Using Napari through Jupyter

    Image Segmentation: Basic Concepts

    Figure 1

    A screenshot of a rough semantic segmentation of nuclei in Napari

    Figure 2

    A screenshot of an instance segmentation of nuclei with some incorrectly joined instances.

    Figure 3

    In the napari toolbar, open Layers > Measure > Regionprops (labels) (skimage). You should see a dialog like this: A screenshot of the napari-skimage Regionprops widget at startup.

    Figure 4

    Click Analyze - a table of numeric values should appear in napari. If it opens in an inconvenient location, you can click and drag on the header containing the x, Napari's hide visibility icon and other icons next to the table window to reposition it. A screenshot of the numeric value table created by the napari-skimage plugin

    Figure 5

    A screenshot of an instance segmentation of nuclei.

    Figure 6

    You may remember from our first lesson that we can change to 3D view mode by pressing the Napari's 2D/3D toggle button. Try it now.

    Figure 7

    A screenshot of an instance segmentation of nuclei in 3D mode with some incorrectly joined instances. You should see the image rendered in 3D, with a clear join between the upper most light purple nucleus and its neighbour.

    Figure 8

    A screenshot region-props dialog highlighting the smallest nucleus.

    Figure 9

    Some nuclei that are touching remain partially connected. Semantic segmentation mask eroded with a ball of radius 5.

    Figure 10

    Erosion with a radius of 10 removes enough pixels to separate touching nuclei
    while still keeping the nuclei you want to analyse. Semantic segmentation mask eroded with a ball of radius 10.

    Figure 11

    Erosion with a radius of 15 is too strong: several nuclei become over‑eroded
    and some disappear completely. Semantic segmentation mask eroded with a ball of radius 15.

    Figure 12

    Instance segmentation on the eroded segmentation mask

    Figure 13

    Dilated instance segmentation on the eroded segmentation mask There are now 19 apparently correctly labelled nuclei that appear to be the same shape as in the original mask image.

    Figure 14

    A comparison between the expanded instance segmentation and the original semantic segmentation showing some mismatch between the borders. Looking at the above image we can see some small mismatches around the edges of most of the nuclei. It should be remembered when looking at this image that it is a single slice though a 3D image, so in some cases where the differences look large (for example the nucleus at the bottom right) they may still be only one pixel deep. Will the effect of this on the accuracy of our results be significant?

    Figure 15

    The instance segmentation with any nuclei crossing the image boundary removed

    Segment Anything for Microscopy (μSAM)

    Figure 1

    Screenshot of LIVECell sample image from micro-sam in Napari

    Figure 2

    Results from our traditional segmentation pipeline

    Figure 3

    Results from our traditional segmentation pipeline with box around a cell that has been over segmented

    Figure 4

    Box prompt

    Figure 5

    Positive point prompt

    Figure 6

    Automatic Segmentation results using μSAM