Volumetric Electron Microscopy images can be used for connectomics, the study of brain connectivity at the cellular level.
A prerequisite for this inquiry is the automatic identification of neural cells, which requires machine learning algorithms and in particular efficient image segmentation algorithms.
In this thesis, we develop new algorithms for this task.
In the first part we provide, for the first time in this
field, a method for training a neural network to predict optimal input data for a watershed algorithm.
We demonstrate its superior performance compared to other segmentation methods of its category.
In the second part, we develop an efficient watershed-based algorithm for weighted graph
partitioning, the \emph{Mutex Watershed}, which uses negative edge-weights for the first time.
We show that it is intimately related to the multicut and has a cutting edge performance on a connectomics challenge.
Our algorithm is currently used by the leaders of two connectomics challenges.
Finally, motivated by inpainting neural networks, we create a method to learn the graph weights without any supervision
