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.

PlantSeg ZooBioImage.IO Model Zoo

• 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

• 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.

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.

• 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.