This thesis presents novel methodological approaches for the automated segmentation of
neurons from electron microscopic image volumes using machine learning techniques. New
potentials for neural segmentation are revealed by incorporating (high-level) biological
prior knowledge. This goes beyond the modeling of neural tissue which has been applied
for the purpose of its segmentation, so far.
Firstly, the V-Multicut algorithm is introduced which enables the consideration of
topological constraints for segmented membranes. In this way, biologically implausible
appearances of membranes are corrected. Secondly, this thesis proves that, in addition to
local evidence and topological requirements for the detection of neural membranes, the
consideration of high-level biological prior knowledge is beneficial. For this task, both the
recently proposed Asymmetric Multiway Cut and the introduced Semantic Agglomerative
Clustering algorithm are implemented and quantitatively evaluated. To be precise, the
spatial separation of dendrites and axons in mammals is exploited to significantly improve
the segmentation quality.
Additionally, new ways to improve the scalability of the used algorithms are presented.
All in all this thesis serves as another step towards fully automated segmentation of
neurons and contributes to the field of connectomics
