Publication details

On generating benchmark datasets for evaluation of segmentation and tracking algorithms in fluorescence microscopy

Investor logo


Year of publication 2013
MU Faculty or unit

Faculty of Informatics

Description In fluorescence microscopy, the proper evaluation of image segmentation and tracking algorithms is still an open problem. As the ground truth for cell image data (and measurements on them) is not available in most experiments, the outputs of different image analysis methods can hardly be verified or compared to each other. We created a toolbox that can generate 3D digital phantoms of specific cellular components along with their corresponding images degraded by specific optics and electronics. The images can represent static scenes (fixed cell) as well as time-lapse sequences (living cells). Such synthetically generated images can serve as a benchmark dataset for measuring the quality of various segmentation and tracking algorithms.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info