# Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts

## TL;DR

This paper introduces a novel graph partitioning approach that incorporates domain-specific biological knowledge to significantly enhance the accuracy of microscopy image segmentation, addressing limitations of current neural network methods.

## Contribution

It presents a method to integrate domain knowledge into the lifted multicut framework, improving segmentation accuracy in electron microscopy images.

## Key findings

- Significant accuracy improvements in EM segmentation tasks.
- Effective use of long-range interactions informed by biological knowledge.
- Enhanced segmentation results over traditional neural network approaches.

## Abstract

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.