CELLPROFILER OUTPUT DURING PROCESSING FULL

This is a combined workflow, which lets you start from the raw data, perform pixel classification as describedĪnd then thresholding the probability maps to obtain a segmentation that you then use in Object Classification. Pixel Classification + Object Classification However, once you have created a satisfactory classifier using one or more small images, you can use the “Blockwise Object Classification”įeature to run object classification on much larger images (prediction only – no training.) This means you can’t use enormous images for training the object classifier.

In the current version of ilastik, computations on the training images are not performed lazily – the entire image is processed at once. Pixel Classification + Object Classification.be generated with the Pixel Classification Workflow.ĭepending on the availability of these segmentation images, the user can choose between three flavors of object classification workflow, which differ by their input data: In order to do so, the workflow needs segmentation images besides the usual raw image data, that can e.g.

CELLPROFILER OUTPUT DURING PROCESSING FULL

Object Classification Object Classification Demo (3 minutes) How it works, what it can doĪs the name suggests, the object classification workflow aims to classify full objects, based on object-level features and user annotations.Īn object in this context is a set of pixels that belong to the same instance.

Cellprofiler output during processing