Hepatic echinococcosis (HE) is a prevalent disease in economically underdeveloped pastoral areas, where adequate medical resources are usually lacking. Existing methods often ignore multi-scale feature fusion or focus only on feature fusion between adjacent levels, which may lead to insufficient feature fusion. To address these issues, we propose HES-UNet, an efficient and accurate model for HE lesion segmentation. This model combines convolutional layers and attention modules to capture local and global features. During downsampling, the multi-directional downsampling block (MDB) is employed to integrate high-frequency and low-frequency features, effectively extracting image details. The multi-scale aggregation block (MAB) aggregates multi-scale feature information. In contrast, the multi-scale upsampling Block (MUB) learns highly abstract features and supplies this information to the skip connection module to fuse multi-scale features. Due to the distinct regional characteristics of HE, there is currently no publicly available high-quality dataset for training our model. We collected CT slice data from 268 patients at a certain hospital to train and evaluate the model. The experimental results show that HES-UNet achieves state-of-the-art performance on our dataset, achieving an overall Dice Similarity Coefficient (DSC) of 89.21%, which is 1.09% higher than that of TransUNet. The project page is available at https://chenjiayan-qhu.github.io/HES-UNet-page.

翻译：肝包虫病（HE）是经济欠发达牧区的一种常见疾病，这些地区通常缺乏充足的医疗资源。现有方法往往忽略多尺度特征融合，或仅关注相邻层级间的特征融合，这可能导致特征融合不充分。为解决这些问题，我们提出了HES-UNet，一种高效且准确的HE病灶分割模型。该模型结合了卷积层与注意力模块，以捕获局部与全局特征。在下采样过程中，采用多向下采样块（MDB）来整合高频与低频特征，有效提取图像细节。多尺度聚合块（MAB）则用于聚合多尺度特征信息。相比之下，多尺度上采样块（MUB）学习高度抽象的特征，并将此信息提供给跳跃连接模块，以融合多尺度特征。由于HE具有明显的区域特征，目前尚无公开的高质量数据集可用于训练我们的模型。我们从某医院的268名患者处收集了CT切片数据，以训练和评估模型。实验结果表明，HES-UNet在我们的数据集上取得了最先进的性能，整体Dice相似系数（DSC）达到89.21%，比TransUNet高出1.09%。项目页面位于https://chenjiayan-qhu.github.io/HES-UNet-page。