EAGLE高效分割肝包虫病病灶，兼顾精度与计算效率。
EAGLE: An Efficient Global Attention Lesion Segmentation Model for Hepatic Echinococcosis
- 采用混合状态空间结构，融合局部与全局特征
- 在260例患者数据上达89.76%的分割准确率
- 适合资源有限地区医疗影像辅助诊断
肝包虫病（HE）是资源匮乏牧区常见寄生虫病。现有基于CNN和Transformer的医学图像分割方法中，CNN受限于局部感受野无法建模全局上下文，而Transformer虽能捕捉长程依赖但计算开销大。近期状态空间模型（SSM）如Mamba因其线性复杂度建模长序列的能力受到关注。本文提出EAGLE，一种基于渐进式视觉状态空间（PVSS）编码器与混合视觉状态空间（HVSS）解码器的U形网络，协同实现肝包虫病病灶的高效高精度分割。所提卷积视觉状态空间块（CVSSB）用于融合局部与全局特征，哈尔小波变换块（HWTB）将空间信息压缩至通道维度，实现无损下采样。由于缺乏公开的HE数据集，研究收集了260例患者的CT切片。实验结果表明，EAGLE达到89.76%的交并比（DSC），优于MSVM-UNet的1.61%。
原文摘要 · Abstract (English)
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%.
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