  Accurate polyp segmentation is of great significance for the diagnosis and
treatment of colorectal cancer. However, it has always been very challenging
due to the diverse shape and size of polyp. In recent years, state-of-the-art
methods have achieved significant breakthroughs in this task with the help of
deep convolutional neural networks. However, few algorithms explicitly consider
the impact of the size and shape of the polyp and the complex spatial context
on the segmentation performance, which results in the algorithms still being
powerless for complex samples. In fact, segmentation of polyps of different
sizes relies on different local and global contextual information for regional
contrast reasoning. To tackle these issues, we propose an adaptive context
selection based encoder-decoder framework which is composed of Local Context
Attention (LCA) module, Global Context Module (GCM) and Adaptive Selection
Module (ASM). Specifically, LCA modules deliver local context features from
encoder layers to decoder layers, enhancing the attention to the hard region
which is determined by the prediction map of previous layer. GCM aims to
further explore the global context features and send to the decoder layers. ASM
is used for adaptive selection and aggregation of context features through
channel-wise attention. Our proposed approach is evaluated on the EndoScene and
Kvasir-SEG Datasets, and shows outstanding performance compared with other
state-of-the-art methods. The code is available at
https://github.com/ReaFly/ACSNet.
