DR-Unet: Rethinking the ResUnet++ Architecture with Dual ResPath skip connection for Nuclei segmentation

Abstract

Nuclei segmentation is a crucial stage in the analysis of cell microscope pictures. By identifying nuclei, researchers may identify and characterize each cell in a sample. Some models used techniques based on encoder-decoder pairs, such as U-Net, Multi ResUnet, DoubleUnet, and ResUnet++, which have been implemented and deployed on the Data Science Bowl 2018 dataset and given excellent results. However, there is still a semantics gap between the features that directly connect from encoder to decoder in ResUnet++, and the extraction of information on many different regions is still limited. To improve the performance of ResUnet++ in this segmentation task, in this paper, we propose a new architecture that uses Double ResPath (DR), called Double respath Unet (DR-Unet). The DR-Unet architecture retains some advantages that made Resunet++ successful such as residual block associated with a squeeze and excitation block. Besides that, we also pass the encoder features through Respath, which can bridge the semantic gap instead of combining the encoder with the decoder feature straightforwardly. Moreover, we use Progressive Atrous Spatial Pyramidal Pooling, PASPP, to replace ASPP to capture contextual information more efficiently. Experimental results demonstrate that DR-Unet outperforms ResUnet, DoubleUnet, and other models in the benchmark.