Cross-Level Adaptive Pyramid Pooling Network for Cardiac Structure Segmentation

The segmentation of cardiac structures in two-dimensional echocardiographic video is crucial for the diagnosis of various cardiac diseases. However, existing video datasets typically contain only sparse annotations for one or two frames. Furthermore, due to the complex variations caused by cardiac motion, the pooling mechanisms employed in current methods result in the loss of global context for the segmentation targets. To address these issues, we propose a Cross-Level Adaptive Pyramid Pooling Network (CAPP) for cardiac image segmentation under sparse annotations. Specifically, the proposed dynamic adaptive network adjusts its architecture based on the size and spacing of input patches from images of different scales, enhances the network's focus on beneficial features through a cross-channel interaction mechanism, and employs data augmentation to increase data complexity, thereby achieving better training outcomes and addressing the segmentation challenges posed by sparse annotations. Additionally, we propose the Cross-Level Pyramid Pooling Module, which captures contextual information between different levels in the network to extract relevant features of cardiac structures. Our method achieves state-of-the-art performance in cardiac structure segmentation on two publicly available echocardiographic datasets.