HES-UNet: 一种用于肝包虫病病灶分割的U-Net
HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation
AI总结：
针对肝包虫病病灶分割中现有方法多尺度特征融合不足且缺乏公开高质量数据集的问题，提出结合卷积与注意力模块、含多方向下采样等模块的HES-UNet模型，在自建268例患者CT数据集上取得SOTA性能。
摘要：
肝包虫病（HE）是经济欠发达牧区的常见疾病，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HES-UNet，一种高效精准的HE病灶分割模型。该模型结合卷积层与注意力模块来捕获局部和全局特征。在下采样阶段，采用多方向下采样块（MDB）整合高频与低频特征，有效提取图像细节；多尺度聚合块（MAB）聚合多尺度特征信息。与之对应，多尺度上采样块（MUB）学习高抽象特征，并将该信息提供给跳跃连接模块以融合多尺度特征。由于HE具有鲜明的地域特征，目前尚无公开的高质量数据集可用于训练我们的模型。我们收集了某医院268名患者的CT切片数据来训练和评估该模型。实验结果表明，HES-UNet在我们的数据集上取得了当前最优性能，整体戴斯相似系数（DSC）达到89.21%，比TransUNet高出1.09%。项目页面可访问https://chenjiayan-qhu.github.io/HES-UNet-page。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
