BBR-Net: Boundary-Balanced Replay for Continual Medical Image Segmentation
This paper proposes BBR-Net, a boundary-balanced replay network that selects samples using boundary-aware priority and class balance to preserve anatomical structure. Evaluated on CAMUS and CardiacNet, forward setting retains source performance and reduces catastrophic forgetting, while reverse setting reveals failure when initial representations are noisy. Structural perturbation analysis shows replay effectiveness depends on stored structural reliability.
[2606.14731] BBR-Net: Boundary-Balanced Replay for Continual Medical Image Segmentation
[Submitted on 2 Jun 2026]
Title:BBR-Net: Boundary-Balanced Replay for Continual Medical Image Segmentation
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Abstract:Continual learning for medical image segmentation remains challenging under domain shift because replay-based methods often preserve appearance information without explicitly modeling anatomical structure. This study investigates whether structural consistency governs knowledge retention in continual cardiac ultrasound segmentation. We propose the Boundary-Balanced Replay Network (BBR-Net), which selects replay samples using boundary-aware priority and class balance to preserve anatomically informative regions. The method is evaluated on CAMUS and CardiacNet under forward (CAMUS to CardiacNet) and reverse (CardiacNet to CAMUS) task orders. In the forward setting, BBR-Net retains source-task performance close to an offline joint-training reference, while markedly reducing catastrophic forgetting and preserving competitive target-task adaptation. Ablation results show that boundary-aware prioritization contributes to retention and improves the balance between source-task preservation and target-task adaptation when combined with class-aware sampling. In contrast, the reverse setting reveals that structure-aware replay fails when initial representations are learned from noisy and structurally inconsistent data. To isolate this effect, we conduct a controlled structural perturbation analysis by progressively corrupting source-task boundaries while keeping the dataset, architecture, and training protocol fixed. Forgetting increases consistently as structural reliability decreases, suggesting that replay effectiveness is strongly influenced by the quality of stored structural information, rather than by memory capacity alone. These findings indicate that preserving anatomical structure under domain shift is a central factor in continual medical image segmentation, and that replay mechanisms should account for structural reliability to support robust knowledge retention.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.14731 [cs.CV]
(or arXiv:2606.14731v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.14731
arXiv-issued DOI via DataCite
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From: Zahid Ullah [view email] [v1] Tue, 2 Jun 2026 04:10:01 UTC (6,827 KB)
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