EGMNet: Error-free and Global Memory Evolution for Breast Ultrasound Video Segmentation

Breast ultrasound video segmentation remains challenging due to severe appearance variations and temporal discontinuity, where lesions may temporarily disappear, change their appearance, or be interfered by newly emerging targets, resulting in inaccurate lesion re-identification and slow segmentation recovery after reappearance. Existing memory-based methods alleviate this issue by storing historical representations, but recursive memory evolution may introduce accumulated update deviations and weaken previously learned lesion information during long-term propagation. Moreover, current-frame-driven memory updating lacks sufficient global temporal awareness, while redundant information in memory states limits representation efficiency. To address these challenges, we propose EGMNet, an efficient memory optimization framework for breast ultrasound video segmentation, consisting of three complementary components. First, we introduce EME, which employs continuous-time dynamics to derive an analytical memory transition process, enabling accurate spatial memory updating with reduced approximation deviation. Second, we develop GMA based on recursive least squares, which formulates memory updating as a projection optimization problem and incorporates global temporal information for adaptive memory evolution. Third, we propose CMC to exploit the intrinsic low-dimensional structure of memory updates, reducing redundant information storage while preserving informative representations. Extensive experiments on the BUS video segmentation dataset demonstrate that EGMNet achieves significant improvements over existing state-of-the-art methods. Besides, The proposed EGMNet provides stronger robustness under lesion disappearance and appearance variation scenarios, enabling rapid target re-capture and more reliable long-term ultrasound video segmentation.