Abstract: In order to realize automatic gland segmentation, reduce the workload of pathologists and help doctors make more accurate clinical decisions, an adaptive-gland-segmentation-net (AGS-net) based on attention mechanism and deformable convolution is proposed. In this model, grouping convolution and attention mechanism were used to make the model more representative. A deformable convolution layer was added to adapt to the glands with different levels of differentiation. In GlaS dataset, the performance of AGS-Net with stain normalization ranked in the top three of the existing algorithms in terms of detection results, segmentation performance and shape similarity, and it had great advantages.