Summary
CalcSeg is a confidence-aware latent context curriculum learning framework for myocardial scar segmentation from single-stack late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging. Scar segmentation in LGE-CMR is clinically important but difficult due to low tissue contrast, diffuse and small scar regions, and limited 3D spatial context in single-stack acquisitions. The method introduces a dynamic semi-supervised curriculum learning strategy that progressively expands training from easier to more challenging scar cases using a learned confidence-aware scoring function. This function combines errors of predicted scar maps, quantified epistemic uncertainty, and scar burden estimates to automatically assess sample difficulty without manual labels. To compensate for limited spatial context in single-stack acquisitions, the authors develop latent slice-wise self-attention that captures inter-slice dependencies and infers 3D spatial representations from sparse 2D inputs. Evaluations on multi-center clinical LGE-CMR datasets show that CalcSeg consistently outperforms competing methods. The work was authored by Nivetha Jayakumar, Hannah Kim, Amit R. Patel, and Miaomiao Zhang, and is available on arXiv as 2608.20305.
Paper Overview
Field: Computer Vision
Authors: Nivetha Jayakumar, Hannah Kim, Amit R. Patel, Miaomiao Zhang
arXiv: 2608.20305
Summary
Myocardial scar segmentation from single-stack late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging is a long-standing and clinically important challenge, particularly in the presence of low tissue contrast, diffuse and small scar regions. These challenges are further compounded by limited 3D spatial context.
This paper proposes CalcSeg, a confidence-aware latent context curriculum learning framework that leverages fused 3D feature representations from single-stack 2D LGE-CMR images for robust scar segmentation.
Key Contributions
- Dynamic semi-supervised curriculum learning: A strategy that progressively expands training from easier to more challenging scar cases using a learned confidence-aware scoring function.
- Confidence-aware scoring function: Integrates errors of predicted scar maps, quantified epistemic uncertainty, and scar burden estimates to automatically assess sample difficulty without requiring manual labels.
- Latent slice-wise self-attention: Developed to compensate for the limited spatial context of single-stack acquisitions by capturing inter-slice dependencies and inferring 3D spatial representations from sparse 2D inputs.
Results
Evaluation on multi-center clinical LGE-CMR datasets demonstrates that CalcSeg consistently outperforms all competing methods.
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*Source link*: https://arxiv.org/abs/2608.20305
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