MDAU-Net: A Liver and Liver Tumor Segmentation Method Combining an Attention Mechanism and Multi-Scale Features
Source abstract
Abstract
In recent years, U-Net and its extended variants have made remarkable progress in the realm of liver and liver tumor segmentation. However, the limitations of single-path convolutional operations have hindered the full exploitation of valuable features and restricted their mobility within networks. Moreover, the semantic gap between shallow and deep features proves that a simplistic shortcut is not enough. To address these issues and realize automatic liver and tumor area segmentation in CT images, we introduced the multi-scale feature fusion with dense connections and an attention mechanism segmentation method (MDAU-Net). This network leverages the multi-head attention (MHA) mechanism and multi-scale feature fusion. First, we introduced a double-flow linear pooling enhancement unit to optimize the fusion of deep and shallow features while mitigating the semantic gap between them. Subsequently, we proposed a cascaded adaptive feature extraction unit, combining attention mechanisms with a series of dense connections to capture valuable information and encourage feature reuse. Additionally, we designed a cross-level information interaction mechanism utilizing bidirectional residual connections to address the issue of forgetting a priori knowledge during training. Finally, we assessed MDAU-Net’s performance on the LiTS and SLiver07 datasets. The experimental results demonstrated that MDAU-Net is well-suited for liver and tumor segmentation tasks, outperforming existing widely used methods in terms of robustness and accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · AI in cancer detection · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
Key Laboratory of Images and Graphics Intelligent Processing of National Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China
Funding
Natural Science Foundation of Ningxia · Basic Scientific Research in the Central Universities of North Minzu University · Graduate Innovation Project of North Minzu University · Image and Intelligent Information Processing Innovation Team of the National Ethnic Affairs Commission
References (33)
Show source-provided references
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