title: Scalable Instance Segmentation for Microscopy
creator: Pape, Constantin
subject: ddc-004
subject: 004 Data processing Computer science
subject: ddc-530
subject: 530 Physics
subject: ddc-570
subject: 570 Life sciences
description: 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 and 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.
date: 2021
type: Dissertation
type: info:eu-repo/semantics/doctoralThesis
type: NonPeerReviewed
format: application/pdf
identifier: https://archiv.ub.uni-heidelberg.de/volltextserverhttps://archiv.ub.uni-heidelberg.de/volltextserver/30147/1/phd-thesis-cpape.pdf
identifier: DOI:10.11588/heidok.00030147
identifier: urn:nbn:de:bsz:16-heidok-301471
identifier:   Pape, Constantin  (2021) Scalable Instance Segmentation for Microscopy.  [Dissertation]     
relation: https://archiv.ub.uni-heidelberg.de/volltextserver/30147/
rights: info:eu-repo/semantics/openAccess
rights: http://archiv.ub.uni-heidelberg.de/volltextserver/help/license_urhg.html
language: eng