MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism
Abstract Accurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Image Processing |
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| Online Access: | https://doi.org/10.1049/ipr2.13266 |
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| _version_ | 1846121436967600128 |
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| author | Xiaoyan Shen Ju Wang Yuhua Zhao Rui Zhou Han Gao Jiakai Zhang Hongming Shen |
| author_facet | Xiaoyan Shen Ju Wang Yuhua Zhao Rui Zhou Han Gao Jiakai Zhang Hongming Shen |
| author_sort | Xiaoyan Shen |
| collection | DOAJ |
| description | Abstract Accurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain tumor MRI segmentation network (MRS‐Net) incorporating an attention mechanism to enhance segmentation accuracy. First, the double residual feature fusion module is utilized to enhance the fusion of feature information between different levels. Second, the Atrous Spatial Pyramid Pooling is introduced as a bridging module of the network to capture the features at different scales of the image, so as to enhance the extraction capability of the network for detailed features. Finally, the inverted residual coordinate attention module replaces the direct splicing in Unet to fuse the large feature information at each level and scale, thus enhancing the model's ability to recognize the spatial location information of brain tumors. The Dice coefficients, positive predictive values (PPVs), sensitivities (Sensitivity) and Hausdorff distance (HD), which are the four evaluation indexes, reach 84.54%, 87.43%, 88.37% and 2.248, respectively, which are improved by 1.85%, 2.11%, 2.88% and 6.0%, respectively, compared with Unet. The experimental results show that MRS‐Net achieves better brain tumor image segmentation. |
| format | Article |
| id | doaj-art-2f45fb2429e24fbc8eceac41b1b544a1 |
| institution | Kabale University |
| issn | 1751-9659 1751-9667 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj-art-2f45fb2429e24fbc8eceac41b1b544a12024-12-16T04:00:31ZengWileyIET Image Processing1751-96591751-96672024-12-0118144542455010.1049/ipr2.13266MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanismXiaoyan Shen0Ju Wang1Yuhua Zhao2Rui Zhou3Han Gao4Jiakai Zhang5Hongming Shen6School of Information Science and Technology Nantong University Nantong ChinaSchool of Information Science and Technology Nantong University Nantong ChinaYancheng Tongzhou Orthopedic Hospital Yancheng ChinaZhang Jian College Nantong University Nantong ChinaJiangsu Cancer Hospital Nanjing ChinaSchool of Information Science and Technology Nantong University Nantong ChinaSchool of Microelectronics and School of Integrated Circuits (Jiangsu Key Laboratory of Semi. Dev. & IC Design, Package and Test) Nantong University Nantong ChinaAbstract Accurate segmentation of brain tumor magnetic resonance imaging (MRI) is crucial for treatment planning. Addressing the challenges of complex tumor structures and inadequate cross‐channel information utilization in Unet‐based segmentation, this paper proposes the multi‐scale residual brain tumor MRI segmentation network (MRS‐Net) incorporating an attention mechanism to enhance segmentation accuracy. First, the double residual feature fusion module is utilized to enhance the fusion of feature information between different levels. Second, the Atrous Spatial Pyramid Pooling is introduced as a bridging module of the network to capture the features at different scales of the image, so as to enhance the extraction capability of the network for detailed features. Finally, the inverted residual coordinate attention module replaces the direct splicing in Unet to fuse the large feature information at each level and scale, thus enhancing the model's ability to recognize the spatial location information of brain tumors. The Dice coefficients, positive predictive values (PPVs), sensitivities (Sensitivity) and Hausdorff distance (HD), which are the four evaluation indexes, reach 84.54%, 87.43%, 88.37% and 2.248, respectively, which are improved by 1.85%, 2.11%, 2.88% and 6.0%, respectively, compared with Unet. The experimental results show that MRS‐Net achieves better brain tumor image segmentation.https://doi.org/10.1049/ipr2.13266image processingimage segmentation |
| spellingShingle | Xiaoyan Shen Ju Wang Yuhua Zhao Rui Zhou Han Gao Jiakai Zhang Hongming Shen MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism IET Image Processing image processing image segmentation |
| title | MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism |
| title_full | MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism |
| title_fullStr | MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism |
| title_full_unstemmed | MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism |
| title_short | MRS‐Net: Brain tumour segmentation network based on feature fusion and attention mechanism |
| title_sort | mrs net brain tumour segmentation network based on feature fusion and attention mechanism |
| topic | image processing image segmentation |
| url | https://doi.org/10.1049/ipr2.13266 |
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