This article explains the basic principle of segmentation for people who are new to imaging.
What is segmentation
Instance vs. Semantic Segmentation
As we can see from the example above, once the pixels have been classified into their various classes identifying objects is relatively easy. However, most objects in an image are not usually so conveniently resolved. In many cases, objects can appear to touch or even overlap.
In such cases, a method that simply classifies pixels to identify contiguous groups would produce only one contiguous mass rather than discreet objects.
Such segmentation is known as semantic segmentation.
Separating this singular mass into individual objects is known as instance segmentation.
Various methods exist in arivis for both semantic & instance segmentation, using both traditional intensity based techniques and new ML and DL tool. Examples of Semantic segmentation in arivis Pro include:
- Threshold based segmenter
- Machine Learning segmenter (using a random forest pixel classifier)
- Deep Learning Segmenter using ONNX models
And instance segmentation tools include:
- Blob Finder
- Region Growing
- Watershed segmenter
- Membrane based segmenter
- Cellpose based segmenter
- Deep Learning segmenter using arivis Cloud Instance models
To find out more about using Deep Learning for instance segmentation, please check the Deep Learning segmentation pipelines article.