Exploiting vector attention and context prior for ultrasound image segmentation

Automatic ultrasound image segmentation is crucial for clinical diagnosis and treatment. However, ultrasound image segmentation is challenging because of the ambiguous structure, incomplete boundary and analogous appearance among different categories. To address above challenges, we propose a flexibly plug-and-play module called vector self-attention layer (VSAL) to conduct long-range spatial and channels reasoning simultaneously. Moreover, it also preserves translational equivariance and considers multi-scale information, by using geometric priors and multi-scale calibration. Besides, a novel context aggregation loss (CAL) is designed to consider the contextual dependences between inter-classes and intra-classes based on context prior. The proposed methods, VSAL and CAL, are flexible enough to be integrated in any CNN-based methods. We validate the effectiveness of the modules on two different ultrasound datasets, multi-target Fetal Apical Four-chamber dataset and one-target Fetal Head dataset. Experiment results reveal significant performance gain when using the proposed modules.