用密集关联网络提升心脏分割的解剖准确性
DAM-Seg: Anatomically accurate cardiac segmentation using Dense Associative Networks
- 通过记忆有限解剖模式实现输入无关的结构约束
- 在CAMUS和CardiacNet数据集上均优于基线方法
- 适合处理低可见度心脏图像的鲁棒分割任务
基于深度学习的心脏分割近年来取得显著进展。许多研究通过引入辅助模块来解决解剖不准确的问题，这些模块或对分割结果后处理，或强制特定点间的一致性以保证解剖正确性。然而，这类方法常增加网络复杂度，需独立训练模块，且在可视性差的情况下缺乏鲁棒性。为此，我们提出一种基于Transformer的新架构，利用密集关联网络学习并保留心脏输入中固有的特定模式。与传统方法不同，该方法限制网络仅记忆一组有限模式，在前向传播中通过这些模式的加权和来强制输出的解剖正确性。由于这些模式与输入无关，模型在可视性差的情况下仍表现出更强鲁棒性。所提方法在两个公开数据集CAMUS和CardiacNet上进行评估，实验结果表明，该模型在所有指标上均持续优于基线方法，展现出在心脏分割任务中的有效性和可靠性。
原文摘要 · Abstract (English)
Deep learning-based cardiac segmentation has seen significant advancements over the years. Many studies have tackled the challenge of anatomically incorrect segmentation predictions by introducing auxiliary modules. These modules either post-process segmentation outputs or enforce consistency between specific points to ensure anatomical correctness. However, such approaches often increase network complexity, require separate training for these modules, and may lack robustness in scenarios with poor visibility. To address these limitations, we propose a novel transformer-based architecture that leverages dense associative networks to learn and retain specific patterns inherent to cardiac inputs. Unlike traditional methods, our approach restricts the network to memorize a limited set of patterns. During forward propagation, a weighted sum of these patterns is used to enforce anatomical correctness in the output. Since these patterns are input-independent, the model demonstrates enhanced robustness, even in cases with poor visibility. The proposed pipeline was evaluated on two publicly available datasets, CAMUS and CardiacNet. Experimental results indicate that our model consistently outperforms baseline approaches across all metrics, highlighting its effectiveness and reliability for cardiac segmentation tasks.
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