Selective feature aggregation network with area-boundary constraints for polyp segmentation

Automatic polyp segmentation is considered indispensable in modern polyp screening systems. It can help the clinicians accurately locate polyp areas for further diagnosis or surgeries. Benefit from the advancement of deep learning techniques, various neural networks are developed for handling the polyp segmentation problem. However, most of these methods neither aggregate multi-scale or multi-receptive-field features nor consider the area-boundary constraints. To address these issues, we propose a novel selective feature aggregation network with the area and boundary constraints. The network contains a shared encoder and two mutually constrained decoders for predicting polyp areas and boundaries, respectively. Feature aggregation is achieved by (1) introducing three up-concatenations between encoder and decoders and (2) embedding Selective Kernel Modules into convolutional layers which can adaptively extract features from different size of kernels. We call these two operations the Selective Feature Aggregation. Furthermore, a new boundary-sensitive loss function is proposed to take into account the dependency between the area and boundary branch, thus two branches can be reciprocally influenced and enable more accurate area predictions. We evaluate our method on the EndoScene dataset and achieve the state-of-the-art results with a Dice of 83.08% and a Accuracy of 96.68%.