ECANet: Enhanced Convolutional Attention Network for Liver Segmentation

Hybrid CNN-Transformer models are widely used in medical image segmentation because they combine CNN-based local feature extraction with Transformer-based global context modeling. Despite their popularity, these models face several challenges, including computational complexity, noise blurring, and information loss. This paper proposes an enhanced convolutional attention network (ECANet) for liver segmentation. ECANet uses a U-shaped architecture with efficient channel-attention-based skip connections. Both the encoder and decoder are constructed using enhanced convolutional Transformer (ECT) blocks, where group convolution is integrated into the convolutional attention module for efficient Token embedding and channel disentanglement, and a Token-wise multi-layer perceptron (MLP) branch is incorporated into the wide-focus module to improve feature representation across channels. Deep supervision and a hybrid of Binary Cross-Entropy (BCE) and Dice loss are used to improve boundary accuracy. We evaluate the proposed model on the publicly available LiTS17 dataset. Experiments show that ECANet outperforms the compared CNN-based and CNN-Transformer baseline models on both quantitative and qualitative measures.