EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis
Journal:
arXiv
Published Date:
Jun 25, 2025
Abstract
Hepatic echinococcosis (HE) is a widespread parasitic disease in
underdeveloped pastoral areas with limited medical resources. While CNN-based
and Transformer-based models have been widely applied to medical image
segmentation, CNNs lack global context modeling due to local receptive fields,
and Transformers, though capable of capturing long-range dependencies, are
computationally expensive. Recently, state space models (SSMs), such as Mamba,
have gained attention for their ability to model long sequences with linear
complexity. In this paper, we propose EAGLE, a U-shaped network composed of a
Progressive Visual State Space (PVSS) encoder and a Hybrid Visual State Space
(HVSS) decoder that work collaboratively to achieve efficient and accurate
segmentation of hepatic echinococcosis (HE) lesions. The proposed Convolutional
Vision State Space Block (CVSSB) module is designed to fuse local and global
features, while the Haar Wavelet Transformation Block (HWTB) module compresses
spatial information into the channel dimension to enable lossless downsampling.
Due to the lack of publicly available HE datasets, we collected CT slices from
260 patients at a local hospital. Experimental results show that EAGLE achieves
state-of-the-art performance with a Dice Similarity Coefficient (DSC) of
89.76%, surpassing MSVM-UNet by 1.61%.
