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Modern microscopy techniques acquire images at very high rates, high spatial resolution and with a large field of view. To analyze the large image data-sets acquired with such microscopes, accurate and scalable automated analysis is desperately needed. A key component is the instance segmentation of structures of interest, such as cells, neurons or organelles. In this thesis, we develop scalable methods for boundary based instance segmentation. We make use of Lifted Multicut graph partitioningdoi:10.11588/heidok.00030147 fatcat:g5aldql5jzhcblqptrgaafcrhq