## 2 Related Work
### Hybrid Segmentation Networks  
Hybrid architectures that combine convolution and self-attention operations have recently shown better performance than pure CNN- or transformer-based architectures in medical image segmentation. [PERSON] et al. [17] introduced TransUNet, which combines transformers and CNN in their U-Net-like architecture for 2D medical image segmentation. Their model consists of a CNN-Transformer hybrid model as the encoder where CNN is first used for generating a feature map from the input image, then the transformer encodes tokenized image patches from the feature map to extract global contexts, and finally a CNN-based decoder that upsamples the encoded features combined with the high-resolution CNN feature maps to enable precise localization. [PERSON] et al. [31] introduced MedT, which consists of a transformer-based encoder and a CNN-based decoder. To overcome the requirement of large-scale datasets by transformer-based models for proper training, they proposed a gated axial-attention model by introducing an additional control mechanism in the self-attention module to train their model properly, even with small-scale datasets in medical imaging applications. [PERSON] et al. [32] proposed a hybrid architecture that consists of a CNN-based encoder to extract feature representations, an efficient deformable transformer-based encoder to model the long-range dependency from the extracted feature maps, and a CNN-based decoder for segmentation. They claimed that their deformable transformer reduces computational and spatial complexities and therefore enables the processing of multi-scale and high-resolution feature maps. [PERSON] et al. [33] proposed a similar architecture to TransUNet but in a 3D version for brain tumor segmentation on 3D MRI images. [PERSON] et al. [34] introduced MCEA-UNet, a 3D segmentation network based on the U-Net architecture, to solve the problem of edge detail feature loss due to insufficient feature extraction from existing methods. Their model consists of a multi-scale feature cascaded attention module to extract multi-scale feature information, an attention-gate mechanism to fuse different levels of feature information, and the deep supervision learning method to optimize segmentation in the decoding process. [PERSON] et al. [12] introduced UNETR, a U-Net-like architecture consisting of a stack of transformers as the encoder to extract feature representations of different layers in the transformer and then merged this with a CNN-based decoder through skip connections to predict the final segmentation. [PERSON] et al. [19] introduced nnFormer, a 3D U-Net-like architecture that exploits the combination of interleaved convolution and self-attention operations and introduces a local and global volume-based self-attention mechanism to learn volume representations. In terms of segmentation performance, nnFormer outperformed UNETR, although nnFormer is much more complex than UNETR in terms of the number of parameters and FLOPs. Recently, [PERSON] et al. [20] introduced UNETR++, a 3D medical segmentation approach that incorporates a novel EPA block that efficiently learns spatial and channel-wise features using a pair of inter-dependent branches based on spatial and channel attention. UNETR++ showed a higher accuracy than UNETR but a slightly lower accuracy than nnFormer in liver segmentation. However, UNETR++ is less complex than both UNETR and nnFormer. Inspired by UNETR++, we introduce G-UNETR++ to further improve accuracy in liver segmentation with a moderate model complexity similar to UNETR.