DARMF-UNet: A dual-branch attention-guided refinement network with multi-scale features fusion U-Net for gland segmentation

Abstract

Accurate gland segmentation is critical in determining adenocarcinoma. Automatic gland segmentation methods currently suffer from challenges such as less accurate edge segmentation, easy mis-segmentation, and incomplete segmentation. To solve these problems, this paper proposes a novel gland segmentation network Dual-branch Attention-guided Refinement and Multi-scale Features Fusion U-Net (DARMF-UNet), which fuses multi-scale features using deep supervision. At the first three layers of feature concatenation, a Coordinate Parallel Attention (CPA) is proposed to guide the network to focus on the key regions. A Dense Atrous Convolution (DAC) block is used in the fourth layer of feature concatenation to perform multi-scale features extraction and obtain global information. A hybrid loss function is adopted to calculate the loss of each segmentation result of the network to achieve deep supervision and improve the accuracy of segmentation. Finally, the segmentation results at different scales in each part of the network are fused to obtain the final gland segmentation result. The experimental results on the gland datasets Warwick-QU and Crag show that the network improves in terms of the evaluation metrics of F1 Score, Object Dice, Object Hausdorff, and the segmentation effect is better than the state-of-the-art network models.