  Medical image segmentation can provide a reliable basis for further clinical
analysis and disease diagnosis. The performance of medical image segmentation
has been significantly advanced with the convolutional neural networks (CNNs).
However, most existing CNNs-based methods often produce unsatisfactory
segmentation mask without accurate object boundaries. This is caused by the
limited context information and inadequate discriminative feature maps after
consecutive pooling and convolution operations. In that the medical image is
characterized by the high intra-class variation, inter-class indistinction and
noise, extracting powerful context and aggregating discriminative features for
fine-grained segmentation are still challenging today. In this paper, we
formulate a boundary-aware context neural network (BA-Net) for 2D medical image
segmentation to capture richer context and preserve fine spatial information.
BA-Net adopts encoder-decoder architecture. In each stage of encoder network,
pyramid edge extraction module is proposed for obtaining edge information with
multiple granularities firstly. Then we design a mini multi-task learning
module for jointly learning to segment object masks and detect lesion
boundaries. In particular, a new interactive attention is proposed to bridge
two tasks for achieving information complementarity between different tasks,
which effectively leverages the boundary information for offering a strong cue
to better segmentation prediction. At last, a cross feature fusion module aims
to selectively aggregate multi-level features from the whole encoder network.
By cascaded three modules, richer context and fine-grain features of each stage
are encoded. Extensive experiments on five datasets show that the proposed
BA-Net outperforms state-of-the-art approaches.
