Adaptive Context Exploration Network for Polyp Segmentation in Colonoscopy Images

Recently, automatic and accurate polyp segmentation has become an emerging yet challenging issue. Although convolutional neural networks (CNNs) exhibit a promising future modality to address this issue, most CNN-based methods highly require extensive labeled data. Unfortunately, there is a lack of large-scale public colorectal polyp segmentation datasets in the clinical community and academia. In this study, we construct a new benchmark dataset, which includes 2163 colonoscopy images and their pixel-wise annotations. Moreover, for intelligent polyp segmentation, we propose a novel adaptive context exploration network (ACENet). Our ACENet follows an encoder-decoder architecture and consists of two key modules, i.e., an attentional atrous spatial pyramid pooling (AASPP) module and an adaptive context extraction (ACE) module. The AASPP fuses semantic features from the encoder, and generates the global guidance information for the following decoder. The ACE captures multi-scale features and aggregates them by a branch-wise attention mechanism. Benefiting from these two modules, our ACENet is capable of adaptively exploring the context features to locate and detect the polyp regions effectively. Extensive experiments on the collected dataset and four publicly available datasets show that the proposed ACENet achieves superior performance on five evaluation metrics over three mainstream categories of the state-of-the-art methods.