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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 an efficient and accurate model for HE lesion segmentation:
A U-Net for Hepatic Echinococcosis Lesion Segmentation (HES-UNet).
HES-UNet 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.
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