# A kinetic approach to consensus-based segmentation of biomedical images

arXiv: https://arxiv.org/abs/2211.05226 · HTML: https://mlsanity.com/paper/2211.05226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 71 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Boltzmann type control of opinion consensus through leaders](https://mlsanity.com/paper/1405.0736/) (2014)
- [The Equivalence of Fourier-based and Wasserstein Metrics on Imaging Problems](https://mlsanity.com/paper/2005.06530/) (2020)
- [Kinetic description of optimal control problems and applications to opinion consensus](https://mlsanity.com/paper/1401.7798/) (2014)
- [Opinion dynamics over complex networks: kinetic modeling and numerical methods](https://mlsanity.com/paper/1604.00421/) (2016)
- [Sparse control of Hegselmann-Krause models: Black hole and declustering](https://mlsanity.com/paper/1802.00615/) (2018)

## Built on by (most cited first, 1 of 1)

- [A Multiscale Kinetic Framework for Image Segmentation: From Particle Systems to Continuum Models](https://mlsanity.com/paper/2605.28619/) (2026)

## Abstract (arXiv)

In this work, we apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. In the presented approach, time-dependent information on the microscopic state of each particle/pixel includes its space position and a feature representing a static characteristic of the system, i.e. the gray level of each pixel. From the introduced microscopic model we derive a kinetic formulation of the model. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach that can be obtained in the quasi-invariant scaling. We exploit the computational efficiency of direct simulation Monte Carlo methods for the obtained Boltzmann-type description of the problem for parameter identification tasks. Based on a suitable loss function measuring the distance between the ground truth segmentation mask and the evaluated mask, we minimize the introduced segmentation metric for a relevant set of 2D gray-scale images. Applications to biomedical segmentation concentrate on different imaging research contexts.