Open Access

ARTICLE

ECANet: Enhanced Convolutional Attention Network for Liver Segmentation

Yuyan Ning^1,2, Haiyun Huang¹, Legend Zhang³, Wei Wei⁴, Hao Quan⁵, Bo Yang^1,*

1 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
2 Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
3 School of Artificial Intelligence, Guangzhou Huashang University, Guangzhou, China
4 School of Mechanical and Material Engineering, Xi’an University, Xi’an, China
5 Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy

* Corresponding Author: Bo Yang. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications-II)

Computer Modeling in Engineering & Sciences 2026, 147(3), 40 https://doi.org/10.32604/cmes.2026.083345

Received 02 April 2026; Accepted 12 May 2026; Issue published 30 June 2026

Abstract

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.

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Keywords

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