Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T
Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency...
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| Vydáno v: | IET image processing Ročník 18; číslo 6; s. 1550 - 1564 |
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01.05.2024
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| ISSN: | 1751-9659, 1751-9667 |
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| Abstract | Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T.
This paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin Transformer stage with a new downsampling transition module called dual path feature reasoning. The entire network framework focuses on for extracting global and local features from multi‐modal data and can capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin Transformer network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. |
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| AbstractList | Abstract Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T. Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T . Tumour medical image segmentation plays a crucial role in clinical imaging diagnosis. Existing research has achieved good results, enabling the segmentation of three tumour regions in MRI brain tumour images. Existing models have limited focus on the brain tumour areas, and the long‐term dependency of features is weakened as the network depth increases, resulting in blurred edge segmentation of the targets. Additionally, considering the excellent segmentation performance of the Swin Transformer(Swin‐T) network, its network structure and parameters are relatively large. To address these limitations, this paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin‐T stage with a new downsampling transition module called dual path feature reasoning (DPFR). The entire network framework is designed to extract global and local features from multi‐modal data, enabling it to capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin‐T network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. The corresponding source codes are available at https://github.com/CurbUni/Brain‐Tumor‐Segmentation‐Framework‐with‐Deep‐Nuanced‐Reasoning‐and‐Swin‐T. This paper proposes a brain tumour segmentation framework with deep nuanced reasoning and Swin‐T. It is mainly composed of the backbone hybrid network (BHN) and the deep micro texture extraction module (DMTE). The BHN combines the Swin Transformer stage with a new downsampling transition module called dual path feature reasoning. The entire network framework focuses on for extracting global and local features from multi‐modal data and can capture and analyze deep texture features in multi‐modal images. It provides significant optimization over the Swin Transformer network structure. Experimental results on the BraTS dataset demonstrate that the proposed method outperforms other state‐of‐the‐art models in terms of segmentation performance. |
| Author | Xu, Yang Qu, Xiaolong Yang, Pan Yin, Li Qi, Guanqiu Yu, Kun Gong, Yifei |
| Author_xml | – sequence: 1 givenname: Yang surname: Xu fullname: Xu, Yang organization: Chongqing University of Posts and Telecommunications – sequence: 2 givenname: Kun surname: Yu fullname: Yu, Kun organization: The Second Affiliated Hospital of Chongqing Medical University – sequence: 3 givenname: Guanqiu orcidid: 0000-0001-9562-3865 surname: Qi fullname: Qi, Guanqiu organization: State University of New York at Buffalo State – sequence: 4 givenname: Yifei surname: Gong fullname: Gong, Yifei organization: University of Toronto – sequence: 5 givenname: Xiaolong surname: Qu fullname: Qu, Xiaolong email: 29352301@qq.com organization: Army Medical University – sequence: 6 givenname: Li surname: Yin fullname: Yin, Li email: yl1@cqu.edu.cn organization: Chongqing University Cancer Hospital – sequence: 7 givenname: Pan orcidid: 0000-0002-1964-2770 surname: Yang fullname: Yang, Pan email: yang.pan@cqmu.edu.cn organization: University of Chinese Academy of Sciences |
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| Cites_doi | 10.1109/CAC51589.2020.9326740 10.1007/s00521-022-06960-9 10.1007/978-3-319-75238-9_38 10.1016/j.bspc.2022.104037 10.1109/IPAS50080.2020.9334937 10.1007/978-3-030-72087-2_11 10.3389/fpubh.2023.1091850 10.1155/2022/7911801 10.3390/diagnostics13050872 10.3389/fnins.2022.1009581 10.1007/s00371-021-02328-7 10.1007/978-3-030-87193-2_11 10.1007/978-3-319-55524-9_14 10.1109/TMI.2023.3261707 10.1109/ITOEC49072.2020.9141847 10.1007/978-3-031-16443-9_39 10.1007/s12553-023-00737-3 10.1007/s10462-022-10245-x 10.1109/CVPR.2015.7298965 10.1016/j.simpat.2023.102769 10.1016/j.inffus.2022.10.022 10.1109/ICPR.2018.8546022 10.1109/CVPR46437.2021.00681 10.1038/s41592-020-01008-z 10.1016/j.bspc.2023.104949 10.1016/j.bspc.2022.103861 10.2478/ausi-2022-0004 10.1109/ICASSP40776.2020.9053405 10.3390/brainsci12060797 10.1007/978-3-030-72087-2_19 10.1109/TMI.2023.3250474 10.1109/ACCESS.2021.3108516 10.1007/978-3-031-08999-2_22 10.1109/WACV51458.2022.00181 10.1007/978-3-031-09002-8_16 10.1186/s12880-022-00931-1 10.1109/ICCV48922.2021.00986 10.1007/978-3-319-24574-4_28 10.1007/s10462-023-10453-z 10.1002/ima.22890 10.1016/j.compbiomed.2022.106474 |
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| References | 2021; 9 2023; 13 2023; 79 2023; 14 2022; 152 2023; 11 2023; 33 2023; 56 2023; 126 2022; 22 2023; 42 2023; 85 2022; 2022 2022 2021 2020 2021; 18 2021; 39 2022; 34 2022; 12 2022; 14 2018 2017 2016 2015 2014 2022; 16 2023; 91 2018; 14 e_1_2_10_23_1 e_1_2_10_46_1 e_1_2_10_21_1 e_1_2_10_44_1 e_1_2_10_42_1 e_1_2_10_40_1 e_1_2_10_2_1 e_1_2_10_4_1 e_1_2_10_18_1 e_1_2_10_6_1 e_1_2_10_16_1 e_1_2_10_39_1 e_1_2_10_8_1 e_1_2_10_13_1 e_1_2_10_34_1 e_1_2_10_11_1 e_1_2_10_32_1 e_1_2_10_30_1 e_1_2_10_51_1 e_1_2_10_29_1 e_1_2_10_27_1 e_1_2_10_25_1 e_1_2_10_48_1 e_1_2_10_24_1 e_1_2_10_45_1 e_1_2_10_22_1 e_1_2_10_43_1 e_1_2_10_20_1 e_1_2_10_41_1 e_1_2_10_52_1 e_1_2_10_3_1 e_1_2_10_19_1 e_1_2_10_5_1 e_1_2_10_17_1 e_1_2_10_38_1 e_1_2_10_7_1 e_1_2_10_15_1 e_1_2_10_36_1 e_1_2_10_12_1 e_1_2_10_35_1 e_1_2_10_9_1 e_1_2_10_10_1 Wang Y. (e_1_2_10_14_1) 2023; 14 e_1_2_10_33_1 e_1_2_10_31_1 e_1_2_10_50_1 Goceri E. (e_1_2_10_37_1) 2018; 14 e_1_2_10_28_1 e_1_2_10_49_1 e_1_2_10_26_1 e_1_2_10_47_1 |
| References_xml | – start-page: 401 year: 2022 end-page: 411 article-title: Evidence fusion with contextual discounting for multi‐modality medical image segmentation – volume: 56 start-page: 2923 issue: 4 year: 2023 end-page: 2969 article-title: Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: A review publication-title: Artif. Intell. Rev. – start-page: 109 year: 2021 end-page: 119 article-title: Transbts: Multimodal brain tumor segmentation using transformer – start-page: 6000 year: 2017 end-page: 6010 article-title: Attention is all you need – year: 2021 – volume: 2022 year: 2022 article-title: Research on segmentation of brain tumor in MRI image based on convolutional neural network publication-title: Biomed Res. Int. – volume: 39 start-page: 2223 issue: 6 year: 2021 end-page: 2233 article-title: X‐NeT: A dual encoding‐decoding method in medical image segmentation publication-title: Visual Comput. – volume: 34 start-page: 5791 issue: 8 year: 2022 end-page: 5812 article-title: Literature review: Efficient deep neural networks techniques for medical image analysis publication-title: Neural Comput. Appl. – year: 2014 – volume: 13 start-page: 181 issue: 2 year: 2023 end-page: 201 article-title: A survey of deep learning for mri brain tumor segmentation methods: Trends, challenges, and future directions publication-title: Health Technol. – start-page: 29 year: 2021 end-page: 36 article-title: Capsule neural networks in classification of skin lesions – start-page: 10012 year: 2021 end-page: 10022 article-title: Swin transformer: Hierarchical vision transformer using shifted windows – volume: 126 year: 2023 article-title: Medical image segmentation method based on multi‐feature interaction and fusion over cloud computing publication-title: Simul. Modell. Pract. Theory – start-page: 234 year: 2015 end-page: 241 article-title: U‐NeT: Convolutional networks for biomedical image segmentation – start-page: 3431 year: 2015 end-page: 3440 article-title: Fully convolutional networks for semantic segmentation – start-page: 144 year: 2020 end-page: 148 article-title: Image augmentation for deep learning based lesion classification from skin images – volume: 91 start-page: 376 year: 2023 end-page: 387 article-title: Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI publication-title: Inf. Fusion – volume: 13 start-page: 872 issue: 5 year: 2023 article-title: Efficient u‐net architecture with multiple encoders and attention mechanism decoders for brain tumor segmentation publication-title: Diagnostics – year: 2022 – volume: 14 start-page: 49 issue: 1 year: 2022 end-page: 74 article-title: U‐NET architecture variants for brain tumor segmentation of histogram corrected images publication-title: Acta Univ. Sapientiae, Inf. – volume: 42 start-page: 2577 issue: 9 year: 2023 end-page: 2591 article-title: One model to synthesize them all: Multi‐contrast multi‐scale transformer for missing data imputation publication-title: IEEE Trans. Med. Imaging – volume: 33 start-page: 1727 issue: 5 year: 2023 end-page: 1744 article-title: Comparison of the impacts of dermoscopy image augmentation methods on skin cancer classification and a new augmentation method with wavelet packets publication-title: Int. J. Imaging Syst. Technol. – start-page: 6881 year: 2021 end-page: 6890 article-title: Rethinking semantic segmentation from a sequence‐to‐sequence perspective with transformers – start-page: 1223 year: 2018 end-page: 1228 article-title: Frelu: Flexible rectified linear units for improving convolutional neural networks – volume: 14 start-page: 125 issue: 1 year: 2018 end-page: 134 article-title: Fully automated and adaptive intensity normalization using statistical features for brain mr images publication-title: Celal Bayar Univ. J. Sci. – year: 2021 article-title: Analysis of capsule networks for image classification – volume: 16 year: 2022 article-title: A medical image segmentation method based on multi‐dimensional statistical features publication-title: Front. Neurosci. – start-page: 118 year: 2021 end-page: 132 article-title: nnu‐net for brain tumor segmentation – start-page: 300 year: 2017 end-page: 304 article-title: Intensity normalization in brain mr images using spatially varying distribution matching – volume: 22 start-page: 1 issue: 1 year: 2022 end-page: 9 article-title: Brain tumour segmentation based on an improved u‐net publication-title: BMC Med. Imaging – start-page: 4804 year: 2020 end-page: 4809 article-title: An instance segmentation algorithm based on improved mask r‐cnn – volume: 79 year: 2023 article-title: dResU‐Net: 3D deep residual U‐Net based brain tumor segmentation from multimodal MRI publication-title: Biomed. Signal Process. Control – volume: 79 year: 2023 article-title: DPAFNET: A residual dual‐path attention‐fusion convolutional neural network for multimodal brain tumor segmentation publication-title: Biomed. Signal Process. Control – volume: 42 issue: 8 year: 2023 article-title: CKD‐TransBTS: Clinical knowledge‐driven hybrid transformer with modality‐correlated cross‐attention for brain tumor segmentation publication-title: IEEE Trans. Med. Imaging – year: 2016 – volume: 12 start-page: 797 issue: 6 year: 2022 article-title: SWINBTS: A method for 3d multimodal brain tumor segmentation using swin transformer publication-title: Brain Sciences – volume: 11 start-page: 200 year: 2023 article-title: MRI brain tumor segmentation using residual spatial pyramid pooling‐powered 3d U‐NeT publication-title: Front. Public Health – start-page: 173 year: 2022 end-page: 186 article-title: Extending nn‐UNeT for brain tumor segmentation – start-page: 215 year: 2021 end-page: 227 article-title: 3d semantic segmentation of brain tumor for overall survival prediction – volume: 152 year: 2022 article-title: Evaluation of denoising techniques to remove speckle and gaussian noise from dermoscopy images publication-title: Comput. Biol. Med. – start-page: 450 year: 2018 end-page: 462 article-title: Ensembles of multiple models and architectures for robust brain tumour segmentation – volume: 85 year: 2023 article-title: Classification of skin cancer using adjustable and fully convolutional capsule layers publication-title: Biomed. Signal Process. Control – volume: 14 start-page: 841 year: 2023 article-title: Fine‐grained weed recognition using swin transformer and two‐stage transfer learning publication-title: Front. Plant Sci. – start-page: 272 year: 2021 end-page: 284 article-title: Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images – start-page: 574 year: 2022 end-page: 584 article-title: Unetr: Transformers for 3d medical image segmentation – volume: 9 start-page: 119881 year: 2021 end-page: 119893 article-title: ETDNET: An efficient transformer deraining model publication-title: IEEE Access – volume: 56 start-page: 12561 year: 2023 end-page: 12605 article-title: Medical image data augmentation: techniques, comparisons and interpretations publication-title: Artif. Intell. Rev. – start-page: 1055 year: 2020 end-page: 1059 article-title: UNet 3+: A full‐scale connected UNet for medical image segmentation – start-page: 1713 year: 2020 end-page: 1720 article-title: Review of research on lightweight convolutional neural networks – volume: 18 start-page: 203 issue: 2 year: 2021 end-page: 211 article-title: nnU‐NeT: A self‐configuring method for deep learning‐based biomedical image segmentation publication-title: Nat. Methods – ident: e_1_2_10_30_1 – ident: e_1_2_10_12_1 doi: 10.1109/CAC51589.2020.9326740 – ident: e_1_2_10_13_1 – ident: e_1_2_10_2_1 doi: 10.1007/s00521-022-06960-9 – ident: e_1_2_10_26_1 doi: 10.1007/978-3-319-75238-9_38 – ident: e_1_2_10_36_1 – ident: e_1_2_10_43_1 doi: 10.1016/j.bspc.2022.104037 – ident: e_1_2_10_49_1 – ident: e_1_2_10_52_1 doi: 10.1109/IPAS50080.2020.9334937 – ident: e_1_2_10_27_1 doi: 10.1007/978-3-030-72087-2_11 – ident: e_1_2_10_25_1 doi: 10.3389/fpubh.2023.1091850 – ident: e_1_2_10_19_1 – ident: e_1_2_10_6_1 doi: 10.1155/2022/7911801 – ident: e_1_2_10_9_1 doi: 10.3390/diagnostics13050872 – ident: e_1_2_10_22_1 doi: 10.3389/fnins.2022.1009581 – ident: e_1_2_10_20_1 – ident: e_1_2_10_21_1 doi: 10.1007/s00371-021-02328-7 – ident: e_1_2_10_31_1 doi: 10.1007/978-3-030-87193-2_11 – ident: e_1_2_10_7_1 doi: 10.1007/978-3-319-55524-9_14 – ident: e_1_2_10_46_1 doi: 10.1109/TMI.2023.3261707 – ident: e_1_2_10_17_1 doi: 10.1109/ITOEC49072.2020.9141847 – ident: e_1_2_10_45_1 doi: 10.1007/978-3-031-16443-9_39 – ident: e_1_2_10_3_1 doi: 10.1007/s12553-023-00737-3 – volume: 14 start-page: 841 year: 2023 ident: e_1_2_10_14_1 article-title: Fine‐grained weed recognition using swin transformer and two‐stage transfer learning publication-title: Front. Plant Sci. – ident: e_1_2_10_5_1 doi: 10.1007/s10462-022-10245-x – ident: e_1_2_10_48_1 – ident: e_1_2_10_23_1 doi: 10.1109/CVPR.2015.7298965 – ident: e_1_2_10_39_1 – volume: 14 start-page: 125 issue: 1 year: 2018 ident: e_1_2_10_37_1 article-title: Fully automated and adaptive intensity normalization using statistical features for brain mr images publication-title: Celal Bayar Univ. J. Sci. – ident: e_1_2_10_18_1 doi: 10.1016/j.simpat.2023.102769 – ident: e_1_2_10_33_1 doi: 10.1016/j.inffus.2022.10.022 – ident: e_1_2_10_34_1 doi: 10.1109/ICPR.2018.8546022 – ident: e_1_2_10_15_1 doi: 10.1109/CVPR46437.2021.00681 – ident: e_1_2_10_28_1 doi: 10.1038/s41592-020-01008-z – ident: e_1_2_10_47_1 doi: 10.1016/j.bspc.2023.104949 – ident: e_1_2_10_38_1 doi: 10.1016/j.bspc.2022.103861 – ident: e_1_2_10_8_1 doi: 10.2478/ausi-2022-0004 – ident: e_1_2_10_40_1 doi: 10.1109/ICASSP40776.2020.9053405 – ident: e_1_2_10_42_1 doi: 10.3390/brainsci12060797 – ident: e_1_2_10_4_1 doi: 10.1007/978-3-030-72087-2_19 – ident: e_1_2_10_32_1 doi: 10.1109/TMI.2023.3250474 – ident: e_1_2_10_11_1 doi: 10.1109/ACCESS.2021.3108516 – ident: e_1_2_10_41_1 doi: 10.1007/978-3-031-08999-2_22 – ident: e_1_2_10_44_1 doi: 10.1109/WACV51458.2022.00181 – ident: e_1_2_10_29_1 doi: 10.1007/978-3-031-09002-8_16 – ident: e_1_2_10_10_1 doi: 10.1186/s12880-022-00931-1 – ident: e_1_2_10_16_1 doi: 10.1109/ICCV48922.2021.00986 – ident: e_1_2_10_24_1 doi: 10.1007/978-3-319-24574-4_28 – ident: e_1_2_10_50_1 doi: 10.1007/s10462-023-10453-z – ident: e_1_2_10_51_1 doi: 10.1002/ima.22890 – ident: e_1_2_10_35_1 doi: 10.1016/j.compbiomed.2022.106474 |
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| Title | Brain tumour segmentation framework with deep nuanced reasoning and Swin‐T |
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