1 research outputs found
Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts
The throughput of electron microscopes has increased significantly in recent
years, enabling detailed analysis of cell morphology and ultrastructure.
Analysis of neural circuits at single-synapse resolution remains the flagship
target of this technique, but applications to cell and developmental biology
are also starting to emerge at scale. The amount of data acquired in such
studies makes manual instance segmentation, a fundamental step in many analysis
pipelines, impossible. While automatic segmentation approaches have improved
significantly thanks to the adoption of convolutional neural networks, their
accuracy still lags behind human annotations and requires additional manual
proof-reading. A major hindrance to further improvements is the limited field
of view of the segmentation networks preventing them from exploiting the
expected cell morphology or other prior biological knowledge which humans use
to inform their segmentation decisions. In this contribution, we show how such
domain-specific information can be leveraged by expressing it as long-range
interactions in a graph partitioning problem known as the lifted multicut
problem. Using this formulation, we demonstrate significant improvement in
segmentation accuracy for three challenging EM segmentation problems from
neuroscience and cell biology