用双向最优令牌匹配提升心脏超声分割的解剖一致性。
BOTM: Echocardiography Segmentation via Bi-directional Optimal Token Matching
- 通过双向最优令牌匹配实现图像片段间的解剖对应
- 在CAMUS2H LV数据集上达到-1.917的豪斯多夫距离，精度更高
- 适合需要高解剖一致性的医学图像分割任务
现有心脏超声分割方法常因形态差异、部分观测和区域强度相似，在低信噪比条件下产生解剖错误的假阳性分割。为在不同帧间提供强解剖约束，我们提出BOTM（双向最优令牌匹配）框架，同步实现分割与解剖结构的最优传输。该方法基于成对超声图像，从新的解剖传输视角学习两组离散图像令牌间的最优对应关系，并将令牌匹配扩展为双向跨传输注意力机制，以在时间域内保持心脏周期变形中的解剖一致性。大量实验表明，BOTM可生成稳定准确的分割结果（如在CAMUS2H LV数据集上豪斯多夫距离为-1.917，在TED数据集上骰子系数提升1.9%），并提供具有解剖一致性保障的匹配解释。
原文摘要 · Abstract (English)
Existed echocardiography segmentation methods often suffer from anatomical inconsistency challenge caused by shape variation, partial observation and region ambiguity with similar intensity across 2D echocardiographic sequences, resulting in false positive segmentation with anatomical defeated structures in challenging low signal-to-noise ratio conditions. To provide a strong anatomical guarantee across different echocardiographic frames, we propose a novel segmentation framework named BOTM (Bi-directional Optimal Token Matching) that performs echocardiography segmentation and optimal anatomy transportation simultaneously. Given paired echocardiographic images, BOTM learns to match two sets of discrete image tokens by finding optimal correspondences from a novel anatomical transportation perspective. We further extend the token matching into a bi-directional cross-transport attention proxy to regulate the preserved anatomical consistency within the cardiac cyclic deformation in temporal domain. Extensive experimental results show that BOTM can generate stable and accurate segmentation outcomes (e.g. -1.917 HD on CAMUS2H LV, +1.9% Dice on TED), and provide a better matching interpretation with anatomical consistency guarantee.
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