Recent models based on convolutionalneuralnetwork (CNN) and transformer have achieved the promising performance for 3D medical image segmentation. However, these methods cannot segment small targets well even when e...
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Recent models based on convolutionalneuralnetwork (CNN) and transformer have achieved the promising performance for 3D medical image segmentation. However, these methods cannot segment small targets well even when equipping large parameters. Therefore, We design a novel lightweight hybrid network that combines the strengths of CNN and transformers (LW-CTrans) and can boost the global and local representation capability at different stages. Specifically, we first design a dynamic stem that can accommodate images of various resolutions. In the first stage of the hybrid encoder, to capture local features with fewer parameters, we propose a multi-path convolution (MPConv) block. In the middle stages of the hybrid encoder, to learn global and local features meantime, we propose a multi-view pooling based transformer (MVPFormer) which projects the 3D feature map onto three 2D subspaces to deal with small objects, and use the MPConv block for enhancing local representation learning. In the final stage, to mostly capture global features, only the proposed MVPFormer is used. Finally, to reduce the parameters of the decoder, we propose a multi-stage feature fusion module. Extensive experiments on 3 public datasets for three tasks: stroke lesion segmentation, pancreas cancer segmentation and brain tumor segmentation, show that the proposed LW-CTrans achieves Dices of 62.35 +/- 19.51%, 64.69 +/- 20.58% and 83.75 +/- 15.77% on the 3 datasets, respectively, outperforming 16 state-of-the-art methods, and the numbers of parameters (2.08M, 2.14M and 2.21M on 3 datasets, respectively) are smaller than the non-lightweight 3D methods and close to the lightweight methods. Besides, LW-CTrans also achieves the best performance for small lesion segmentation.
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