> For the complete documentation index, see [llms.txt](https://docs.scenery.graphics/sciview/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.scenery.graphics/sciview/complex-data/segmentations.md).

# 3D Segmentations

We will explore this demo using IntelliJ, partially because the benefit of auto-complete is quite signficant for these more complex tasks. The latest version of this demo can be found [here](https://github.com/scenerygraphics/sciview/blob/master/src/main/java/sc/iview/commands/demo/advanced/ShowSegmentationDemo.java)

![](/files/-MNJll2G4YwvBY6IwJY_)

This demo begins with a demo image. The details of how this image is generated aren't important, but it creates a tuneable number of spheres in random positions within a `(100,100,100)` image (aka RandomAccessibleInterval).

![](/files/-MNJll2HrhleNgFs4poh)

We then display this image as a volume in sciview

![](/files/-MNJll2I8MGpk_msGX9M)

![](/files/-MNJll2J7uGOMQB-pnMS)

Now comes a key step, we perform what is called a "Connected Components Analysis" (aka CCA), which assigns all connected pixels to a specific label. Each of these labels represents a segmentation.

Note: one nuance of this is that if 2 of our randomly generated spheres overlap, they will be treated as being connected.

An alternative would have been to use the pixel values of the image to create each `LabelRegion`. However, in practice that is not a luxury that an image analyst has.

![](/files/-MNJll2K56GGVa52bhsz)

The next thing to do is to create meshes for each segmentation label that we have created. We do this by looping over all `LabelRegion`s that we've created and computing a mesh.

![](/files/-MNJll2Lg92QaqsucYAP)

![](/files/-MNJll2MzCkYf9Ns-Tvs)


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