A deep learning-based method for multiorgan functional tissue units segmentation

Functional tissue region segmentation is the segmentation and example description of tissue epithelium, glandular cavity, fiber and other tissues in the image, which helps to accelerate the understanding of the relationship between cells and tissues in the world. By better understanding the relationship between cells, researchers will have a deeper understanding of cell functions that affect human health. Based on convolutional neural networks, we combine the structural advantages of UNet and EficientNet to create an organ tissue segmentation model. The model fuses the UNet structure with the EficientNet structure, and extracts features with the help of the pre-trained EficientNet optimal structure to improve the ability of feature learning. At the same time, the fusion of multi-scale features in the network is realized through the jump connection, and the segmentation accuracy of the model is improved. We compare our model with other models using the metrics of dice similarity efficiency. our Unet2.5D (ConvNext+ Se_resnet101) owns the highest DSC 0.702 among these models, which is 0.052, 0.024, 0.052 higher than Unet(ResNet50), Unet(Se_Resnet101), Unet(ResNet101) respectively.