Cascaded LGE-MRI Myocardial Fibrosis Segmentation With Dirichlet Output Parametrization and Discretization-Step Aware Regularization

Authors

  • Evgeniy Shchetinin Sevastopol State University, Russian Federation
  • Vladislav Ruzmanov Sevastopol State University, Russian Federation
  • Andrey Shevchuk Sevastopol State University, Russian Federation

DOI:

https://doi.org/10.32603/2071-2340-2026-2-43-56

Keywords:

LGE-MRI, myocardial fibrosis, segmentation, Dirichlet distribution, uncertainty estimation, cascaded neural networks, normalized finite differences

Abstract

Introduction. Automatic myocardium and post-infarction scar segmentation in LGE-MRI is clinically important for quantitative assessment, yet it is sensitive to anisotropic acquisition and domain shift. The aim of this study is to evaluate whether a cascaded design with uncertainty-aware output and discretization-step-aware regularization improves segmentation accuracy and robustness.
Methods. We propose FibroSegNet, a three-stage slice-wise cascade (CropNet, MyoSegNet, ScarSegNet) where ScarSegNet outputs are Dirichlet-parameterized and regularized by Rh, a finite-difference term normalized by physical grid spacing. The model is evaluated on EMIDEC (n=20) against 2D nnU-Net, 2D Swin UNETR, and 3D nnU-Net under the same protocol, with external validation on MyoPS (n=25), ablation studies, and anisotropic resampling tests.
Results. On EMIDEC, FibroSegNet reached Dice 0.891/0.845 (myocardium/scar) versus 0.864/0.812 (2D nnU-Net), 0.868/0.819 (Swin UNETR), and 0.838/0.776 (3D nnU-Net); all myocardium Dice differences remained significant after Bonferroni correction (pBonf < 0.004). On MyoPS without retraining, myocardium performance remained better (+0.032, p < 0.001) while scar differences were not significant (p = 0.38); ablation indicated cascade dominance, Dirichlet benefits for calibration (ECE reduced by 0.026) and error detection (AUC ROC 0.979), and a 1.28 mm HD95 reduction from Rh.
Discussion. The results show that cascade decomposition is the main contributor under strong LGE-MRI anisotropy, while uncertainty maps are primarily valuable for reliability control rather than overlap gain. The main limitation is cross-domain scar transferability, motivating target-domain fine-tuning and domain adaptation.

Author Biographies

  • Evgeniy Shchetinin, Sevastopol State University, Russian Federation

    Dr. Sci. (Phys.-Math.), Professor at Department of Information Technologies and Systems, Sevastopol State University, riviera-molto@mail.ru

  • Vladislav Ruzmanov, Sevastopol State University, Russian Federation

    Master's Degree Student in Computer Science and Engineering, Sevastopol State University, akautneto@gmail.com

  • Andrey Shevchuk, Sevastopol State University, Russian Federation

    Postgraduate, Department of Information Technologies and Systems, Sevastopol State University, andreiluck11@yandex.ru

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Published

2026-06-30

Issue

No. 2 (2026)

Section

Artificial intelligence and machine learning

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Copyright (c) 2026 Evgeniy Shchetinin, Vladislav Ruzmanov, Andrey Shevchuk

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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How to Cite

[1]
E. Shchetinin, V. Ruzmanov, and A. Shevchuk, “Cascaded LGE-MRI Myocardial Fibrosis Segmentation With Dirichlet Output Parametrization and Discretization-Step Aware Regularization”, Comput. Tools Educ., no. 2, pp. 43–56, Jun. 2026, doi: 10.32603/2071-2340-2026-2-43-56.

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