Abstract
Although UNet has proven its success in various tasks involving medical image segmentation, its capacity to capture global context is restricted by the finite receptive field inherent to convolutional operations. Transformer is capable of capturing long-range dependencies. Consequently, integrating transformer into UNet can alleviate the issue of its limited receptive field. However, transformer usually relies heavily on large-scale pre-training and struggles to capture local features. To address these challenges, we propose SimEANet, a network that employs an encoder-decoder structure with a hybrid CNN-Transformer architecture. We design an enhanced ResNet as a shallow feature extractor for the encoder. Furthermore, we introduce SimEA transformer as the backbone of the encoder. Finally, we use improved cascaded upsampling processors to obtain the segmentation result. The performance of SimEANet is substantiated through rigorous testing on two public accessible datasets. Extensive experiments demonstrate the high competitiveness of our approach, achieving average Dice Similarity Coefficients (DSC) of 82.35% and 91.85% on two datasets. SimEANet notably enhances performance in multi-organ segmentation tasks, achieving an advanced level of segmentation accuracy.