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Convolutional neural networks (CNN) (e.g., UNet) have become the de facto standard and attained immense success in medical image segmentation. However, CNN based methods fail to build long-range dependencies and global context connections due to the limited receptive field of the convolution operation. Therefore, Transformer variants have been proposed for medical image segmentation tasks due to their innate capability of capturing long-range correlations through the attention mechanism. However, since Transformers are not designed to capture local information, object boundaries are not well preserved, especially in difficult segmentation scenarios with partly overlapping objects. To address this issue, we propose a contextual attention network that includes a boundary representation on top of the CNN and Transformer features. It utilizes an CNN encoder to capture local semantic information and …