A kinetic approach to consensus-based segmentation of biomedical images
This work addresses segmentation challenges in biomedical imaging, but it appears incremental as it adapts existing consensus models to this domain.
The authors tackled biomedical image segmentation by applying a kinetic bounded confidence consensus model to 2D grayscale images, achieving segmentation through parameter optimization with a loss function comparing predicted and ground truth masks.
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.