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Glandular Cell Segmentation Method Combined with Dense Connective Blocks and Self-Attention Mechanism

Currently used cell segmentation methods are easily to cause the problem of missegmentation and impreciseness for glandular cell segmentation. A glandular cell segmentation model based on U-Net network is proposed which combines dense connective blocks and self-attention mechanism. Firstly, the convolution layers in the U-Net structure are combined to form the dense connective blocks, so that the information can be extracted from the image at different scales. Then the self-attention mechanism is introduced at the decoder to establish a rich context-dependent model for local features to suppress unnecessary feature propagation and improve the accuracy of glandular cell segmentation. The experimental results on the 2015 MICCAI Gland Segmentation Challenge dataset show that the proposed model, with a small number of extra parameters, can achieve improved performance in terms of F1-score, Mean Dice coefficient, and Hausdorff distance compared with other U-Net based methods.