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Segmentation

The segmentation workflow consists of three main steps:

  • Boundary Prediction
  • Boundary to Superpixels
  • Superpixels to Segmentation

Widget: 1. Boundary Predictions

Choose one of the build-in PlantSeg models, or one from the BioImage.IO Model Zoo.

Alternatively, you can import your own model by choosing ADD CUSTOM MODEL from the model selection drop-down menu.

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  • Mode: Select the mode to run the prediction.
  • Model filter: Choose to only show models tagged with `plantseg`.
  • PlantSeg model: Select a pretrained model. Current model description: Unet trained on confocal images of Arabidopsis Ovules on 1/2-resolution in XY with BCEDiceLoss.
  • Show advanced parameters: Change the patch shape, halo shape, and batch size.
  • Device: None

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  • Mode: Select the mode to run the prediction.
  • Model filter: Choose to only show models tagged with `plantseg`.
  • BioImage.IO model: Select a model from BioImage.IO model zoo.
  • Show advanced parameters: Change the patch shape, halo shape, and batch size.
  • Device: None

Widget: 2. Boundary to Superpixels

Here, the boundary prediction is turned into superpixels by using distance transform watershed.

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None
  • Mode: Define if the Watershed will run slice by slice (faster) or on the full volume (slower).
  • Boundary threshold: A low value will increase over-segmentation tendency and a large value increase under-segmentation tendency.
  • Minimum superpixel size: Minimum superpixel size allowed in voxels.
  • Show advanced parameters: Show advanced parameters for the Watershed algorithm.

Widget: 3. Superpixels to Segmentation

DT Watershed tends to over-segment the image, therefor an agglomeration algorithm is used in this third step.

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  • Agglomeration mode: Select which agglomeration algorithm to use.
  • Under/Over segmentation factor: A low value will increase under-segmentation tendency and a large value increase over-segmentation tendency.
  • Minimum segment size: Minimum segment size allowed in voxels.