Abstract: Medical image segmentation is vital for medical image processing. With the development of deep learning,
image segmentation techniques have achieved remarkable development. However, there remain fuzzy and inaccurate
problems in the discrimination of contour pixels for lesion features. To address the problems, we proposed a contour
enhanced attention (CEA) module. It can obtain rich location information by feature encoding in two different
directions and strengthen contours by calculating the offset between location features and input features. Furthermore,
we constructed a U-Net for medical image segmentation based on the proposed module, it can break through the space
limitation of convolution kernel, thus capturing position-aware cross-channel information and clearer edge contour
information. In doing so, the accuracy of segmentation can be improved. Experiments on the public Kvasir-SEG dataset demonstrates that the network with CEA module achieves better results in Dice, precision, recall rate, and
other evaluation indexes in medical segmentation.

Key words: medical image segmentation, location information, attention mechanism, edge contour, contour
differences