HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation

Jiayan Chen; Kai Li; Zhanjin Wang; Zhan Wang; Jianqiang Huang

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arXiv · 2412.06530

HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation

Jiayan Chen·Kai Li·Zhanjin Wang·Zhan Wang·Jianqiang Huang

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Abstract

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.

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Jiayan Chen, Kai Li, Zhanjin Wang, Zhan Wang, Jianqiang Huang. 2024-12-09. HES-UNet: A U-Net for Hepatic Echinococcosis Lesion Segmentation. https://arxiv.org/abs/2412.06530

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