用神经元胞自动机修复分割图的断裂问题，无需定制规则。
rNCA: Self-Repairing Segmentation Masks
- 用局部迭代更新的NCA模型修复分割掩码
- 对视网膜血管提升2-3%的Dice分数，减少60%的β₀错误
- 零样本下修复61.5%的心肌断裂，适合各类分割任务
准确预测拓扑正确的分割掩码仍是通用分割模型的难题，常出现碎片化或不连通输出。传统修复依赖手工设计规则或特定任务架构。本文展示神经元胞自动机（NCA）可直接作为有效修复机制，通过图像上下文引导的局部、迭代更新来修复分割掩码。在不完美掩码与真实标签上训练后，该自动机仅依赖局部信息学习目标形状的结构特性。应用于粗略全局预测的掩码时，其动态过程逐步连接断开区域、剔除松散碎片，最终收敛至拓扑一致结果。实验表明，rNCA可广泛适配不同基础分割模型和任务：对碎片化视网膜血管，实现Dice/clDice提升2-3%，β₀错误降低60%，β₁降低20%；对心肌分割，在零样本设置下修复61.5%的断裂案例，ASSD与HD分别降低19%和16%。验证了NCA作为高效且普适的修复工具的潜力。
原文摘要 · Abstract (English)
Accurately predicting topologically correct masks remains a difficult task for general segmentation models, which often produce fragmented or disconnected outputs. Fixing these artifacts typically requires hand-crafted refinement rules or architectures specialized to a particular task. Here, we show that Neural Cellular Automata (NCA) can be directly re-purposed as an effective refinement mechanism, using local, iterative updates guided by image context to repair segmentation masks. By training on imperfect masks and ground truths, the automaton learns the structural properties of the target shape while relying solely on local information. When applied to coarse, globally predicted masks, the learned dynamics progressively reconnect broken regions, prune loose fragments and converge towards stable, topologically consistent results. We show how refinement NCA (rNCA) can be easily applied to repair common topological errors produced by different base segmentation models and tasks: for fragmented retinal vessels, it yields 2-3% gains in Dice/clDice and improves Betti errors, reducing $β_0$ errors by 60% and $β_1$ by 20%; for myocardium, it repairs 61.5% of broken cases in a zero-shot setting while lowering ASSD and HD by 19% and 16%, respectively. This showcases NCA as effective and broadly applicable refiners.
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