# The Minimum Cost Connected Subgraph Problem in Medical Image Analysis

arXiv: https://arxiv.org/abs/1606.06135 · HTML: https://mlsanity.com/paper/1606.06135/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Connectivity-constrained Interactive Panoptic Segmentation](https://mlsanity.com/paper/2212.06756/) (2022)
- [Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images](https://mlsanity.com/paper/2605.00538/) (2026)

## Abstract (arXiv)

Several important tasks in medical image analysis can be stated in the form of an optimization problem whose feasible solutions are connected subgraphs. Examples include the reconstruction of neural or vascular structures under connectedness constraints. We discuss the minimum cost connected subgraph (MCCS) problem and its approximations from the perspective of medical applications. We propose a) objective-dependent constraints and b) novel constraint generation schemes to solve this optimization problem exactly by means of a branch-and-cut algorithm. These are shown to improve scalability and allow us to solve instances of two medical benchmark datasets to optimality for the first time. This enables us to perform a quantitative comparison between exact and approximative algorithms, where we identify the geodesic tree algorithm as an excellent alternative to exact inference on the examined datasets.