Publications

Papers and preprints

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2026

A Computational Method for Image Segmentation Based on a Dirichlet Field and an Analysis of the Asymptotic Accuracy of Spatial Regularizer Discretization

Modeling, Optimization and Information Technology

Evgeny Yuryevich Shchetinin, Andrey Andreyevich Shevchuk

This paper presents a Dirichlet-field formulation of segmentation with closed-form uncertainty estimation, edge-aware smoothing, and an explicit asymptotic analysis of the spatial regularizers used in the model. Numerical experiments on ACDC, Synapse, and CHAOS show improved calibration and segmentation quality with moderate extra cost.

  • Numerical Methods
  • Applied Systems

DOI10.26102/2310-6018/2026.54.3.009

2026

Global Existence, Invariant Region, and Enhanced Regularity for an Anisotropic Reaction-Diffusion Model of Myocardial Remodeling in Terms of Dirichlet Concentration Parameters

Zenodo preprint

Evgeny Yuryevich Shchetinin, Andrey Andreyevich Shevchuk

A multicomponent anisotropic reaction-diffusion model for post-infarction myocardial remodeling is formulated in terms of Dirichlet concentration parameters so that composition and uncertainty are represented together. The paper proves global existence, establishes invariant-region bounds, and derives enhanced regularity and short-time unconditional uniqueness under additional assumptions.

  • Nonlinear PDEs
  • Applied Systems

DOI10.5281/zenodo.19636807

2026

Semantic Segmentation with Uncertainty Estimation Based on the Dirichlet Model and Anisotropic Regularization

Computational Mathematics and Information Technologies

Evgeny Yuryevich Shchetinin, Andrey Andreyevich Shevchuk

The paper proposes a semantic segmentation method that couples Dirichlet uncertainty modeling with anisotropic regularization. It proves Gamma-convergence and equicoercivity for the discrete energy family and reports calibrated uncertainty estimation with modest computational overhead.

  • Numerical Methods
  • Applied Systems

DOI10.23947/2587-8999-2026-10-1-7-20