通过自监督时序正则化，提升心脏超声分割的时序一致性并自动映射AHA分区。
Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mapping
- 引入自监督时序正则化，利用图像序列时间连贯性优化分割结果。
- 在CAMUS数据集上实现时序一致分割，保持解剖对应关系并自动完成AHA 17分区映射。
- 无需逐帧标注，适合临床心脏超声分析与病理运动模式检测。
基于图的冠状动脉分割方法通过隐式解剖对应关系提供拓扑保障和群体水平分析能力，但独立训练各帧图像序列的模型存在时序不连续问题，影响可靠临床测量，尤其在心脏超声中更为明显。本文提出一种自监督时序正则化作为后训练优化阶段，利用图像序列的时间连贯性，在不依赖逐帧标注的前提下，强制实现时序一致的分割与运动估计。通过惩罚相邻帧间的速度和加速度不连续性，该方法在保持已学习解剖对应关系的同时，显著提升时序一致性。进一步利用这些对应关系，自动将关键点映射至AHA 17段临床标准，支持标准化区域评估及病理性心肌运动模式检测。在CAMUS数据集上的验证表明，结合时序一致性与自动分区映射具有显著临床价值。代码已公开于 https://github.com/david-montalvoo/MaskHybridGNet-TempReg。
原文摘要 · Abstract (English)
Graph-based cardiac segmentation with implicit anatomical correspondences provides topological guarantees and population-level analysis capabilities, but models trained on independent frames of image sequences exhibit temporal discontinuities that affect reliable clinical measurements, particularly in cardiac ultrasound. In this work, we introduce self-supervised temporal regularization as a post-training refinement stage that exploits the temporal coherence in image sequences to enforce consistent cardiac segmentation and motion estimation over time, without requiring per-frame annotations. By penalizing velocity and acceleration discontinuities across consecutive frames, our method achieves temporally consistent segmentations while maintaining the learned anatomical correspondences. We further leverage these correspondences to automatically map landmarks to the AHA 17-segment clinical standard, enabling standardized regional assessment and detection of pathological myocardial motion patterns. Validation on CAMUS dataset demonstrates the clinical utility of combining temporal consistency with automatic regional mapping. The code is publicly available at https://github.com/david-montalvoo/MaskHybridGNet-TempReg
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