MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866915004010201088 |
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| author | Shen, Longfeng Hou, Yanqi Chen, Jiacong Diao, Liangjin Duan, Yaxi |
| author_facet | Shen, Longfeng Hou, Yanqi Chen, Jiacong Diao, Liangjin Duan, Yaxi |
| contents | Accurate segmentation of brain tumors plays a key role in the diagnosis and treatment of brain tumor diseases. It serves as a critical technology for quantifying tumors and extracting their features. With the increasing application of deep learning methods, the computational burden has become progressively heavier. To achieve a lightweight model with good segmentation performance, this study proposes the MBDRes-U-Net model using the three-dimensional (3D) U-Net codec framework, which integrates multibranch residual blocks and fused attention into the model. The computational burden of the model is reduced by the branch strategy, which effectively uses the rich local features in multimodal images and enhances the segmentation performance of subtumor regions. Additionally, during encoding, an adaptive weighted expansion convolution layer is introduced into the multi-branch residual block, which enriches the feature expression and improves the segmentation accuracy of the model. Experiments on the Brain Tumor Segmentation (BraTS) Challenge 2018 and 2019 datasets show that the architecture could maintain a high precision of brain tumor segmentation while considerably reducing the calculation overhead.Our code is released at https://github.com/Huaibei-normal-university-cv-laboratory/mbdresunet |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_01896 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network Shen, Longfeng Hou, Yanqi Chen, Jiacong Diao, Liangjin Duan, Yaxi Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Accurate segmentation of brain tumors plays a key role in the diagnosis and treatment of brain tumor diseases. It serves as a critical technology for quantifying tumors and extracting their features. With the increasing application of deep learning methods, the computational burden has become progressively heavier. To achieve a lightweight model with good segmentation performance, this study proposes the MBDRes-U-Net model using the three-dimensional (3D) U-Net codec framework, which integrates multibranch residual blocks and fused attention into the model. The computational burden of the model is reduced by the branch strategy, which effectively uses the rich local features in multimodal images and enhances the segmentation performance of subtumor regions. Additionally, during encoding, an adaptive weighted expansion convolution layer is introduced into the multi-branch residual block, which enriches the feature expression and improves the segmentation accuracy of the model. Experiments on the Brain Tumor Segmentation (BraTS) Challenge 2018 and 2019 datasets show that the architecture could maintain a high precision of brain tumor segmentation while considerably reducing the calculation overhead.Our code is released at https://github.com/Huaibei-normal-university-cv-laboratory/mbdresunet |
| title | MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.01896 |