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The PSEG tool implements a framework for image segmentation workflows, both for static and dynamic data. Currently, it offers solutions for different scenarios, namely:

1.PERCIST: Semi-automatic lesion segmentation and assessment of static data according to the PERCIST (PET Response Criteria in Solid Tumors) [1,2] methodology. As an additional feature, texture analysis can be applied within the detected lesions.

2.ORGAN SEPARATION: Semi-automatic segmentation of dynamic rodent PET studies into functional organs within only a few minutes.

3.CLUSTERING (K MEANS):  Automatic segmentation of dynamic data into clusters of "kinetically similar" pixels using the k-means algorithm.

4.CLUSTERING (Supervised):  Automatic segmentation of dynamic data into clusters of "kinetically similar" pixels corresponding to a set of prescribed time-activity curves (TACs).

5.MACHINE LEARNING: Automatic segmentation of input image based on a Trained Network. It is mandatory to use input image with the same characteristics as the training images.