Efficient Algorithms for Moral Lineage Tracing

TL;DR

This paper introduces efficient primal feasible local search algorithms and improvements to the branch-and-cut method for the moral lineage tracing problem, enabling accurate and scalable cell lineage analysis in microscopy images.

Contribution

It presents the first efficient primal feasible local search algorithms for MLTP and enhances the branch-and-cut algorithm with tighter cuts and primal integration.

Findings

Algorithms find accurate solutions on existing instances.

Methods scale to larger, more complex instances.

Improved algorithms outperform previous approaches.

Abstract

Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task. Jug et al. have proposed a mathematical abstraction of this task, the moral lineage tracing problem (MLTP), whose feasible solutions define both a segmentation of every image and a lineage forest of cells. Their branch-and-cut algorithm, however, is prone to many cuts and slow convergence for large instances. To address this problem, we make three contributions: (i) we devise the first efficient primal feasible local search algorithms for the MLTP, (ii) we improve the branch-and-cut algorithm by separating tighter cutting planes and by incorporating our primal algorithms, (iii) we show in experiments that our algorithms find accurate solutions on the problem instances of Jug et al. and scale to larger instances, leveraging moral lineage tracing to practical significance.