Keypoint-Guided Medical Video Segmentation Model With Spatiotemporal Feature Fusion

Atrial fibrillation, characterized by high prevalence and poor prognosis, presents a significant global health burden. Accurate segmentation and measurement of left ventricular and left atrial appendage morphology and function are essential for reliable risk assessment. However, these tasks are hindered by ambiguous boundaries, complex cardiac motion, and sparse annotations. To address these challenges, we propose a Keypoint-Guided Medical Video Segmentation Model with Spatiotemporal Feature Fusion (KG-STS). First, we propose a shape-constrained point encoder that explicitly encodes boundary points to improve the representation of ambiguous boundaries. Next, we introduce a motion-aware alignment module that models cardiac motion by forming coherent motion information across frames. Building on these two modules, we develop a keypoint-guided spatiotemporal feature fusion module that integrates spatial boundary representations with temporal motion cues to enhance decoding features under sparse annotations, enabling temporally consistent segmentation and supporting morphological measurement. We evaluate the segmentation and measurement performance of our method on a self-constructed multi-view transesophageal echocardiography dataset and two publicly available transthoracic echocardiography datasets. The results demonstrate that KG-STS achieves superior temporal consistency in segmentation and higher accuracy in morphological measurements compared to competing methods.