AUTOMATIC COLORECTAL GLAND SEGMENTATION ALGORITHM BASED ON DEEP LEARNING

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.