EEA-UNet: An efficient element-wise adaptive attention-based network for abdominal multi-organ segmentation

Abstract

Accurate X-ray computed tomography (CT) image segmentation of the abdominal organs is a key task in automated medical image analysis, with crucial applications in clinical decision-making, computer-aided diagnosis, and surgical planning. However, existing methods still face significant challenges: insufficient capability in modeling long-range contextual dependencies, hindering the adaptability to the complicated morphological variations and spatial relationships of abdominal organs; and inaccurate boundary segmentation due to blurred edges and irregular anatomical structures, particularly in regions with high tissue adhesiveness. To address these issues, we propose an efficient abdominal multi-organ segmentation model, EEA-UNet. Specifically, we design an efficient element-wise adaptive (EEA) attention mechanism integrated into the skip connections to enhance inter-organ feature interactions while maintaining computational efficiency. This module effectively expands the receptive field, improving long-range dependency modeling. An enhanced multi-scale feature fusion (EMF) module is introduced to strengthen decoding capability, coupled with an edge-awareness composite loss function to optimize segmentation accuracy for small organs and boundary regions.

Experimental results on the Synapse dataset demonstrate the competitive performance of EEA-UNet, achieving a Dice score of 84.45% and an HD95 of 0.16. Our method demonstrates a favorable trade-off between segmentation accuracy and computational efficiency, showing improved results compared with several existing approaches in both visual comparison and quantitative metrics.