Scalable Instance Segmentation for Microscopy

Constantin Pape
2021
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 partitioning
more » ... nd develop a method achieving state-of-the-art results on challenging benchmark data-sets. In order to scale this approach up, we introduce a new approximate solver for Multicut and Lifted Multicut, which can solve problems that were previously infeasible. We further establish a method to incorporate domain knowledge into the segmentation problem, which can significantly improve quality. To overcome the brittleness of seeded watersheds, used extensively in segmentation for microscopy, we introduce the Mutex Watershed. This efficient algorithm can segment images directly from pixels without the need for seeds or thresholds. Finally, we apply our methods in collaborative work, demonstrating their utility to answer biological research questions. In summary, our contributions enable scalable instance segmentation, thus eliminating one of the major obstacles to the automated analysis of large microscopy data-sets.
doi:10.11588/heidok.00030147 fatcat:g5aldql5jzhcblqptrgaafcrhq