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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.14418 |
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| _version_ | 1866913582816428032 |
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| author | Jiang, Lan Zheng, Yuchao Yu, Miao Zhang, Haiqing Aladwani, Fatemah Perelli, Alessandro |
| author_facet | Jiang, Lan Zheng, Yuchao Yu, Miao Zhang, Haiqing Aladwani, Fatemah Perelli, Alessandro |
| contents | Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) for precise segmentation. The model utilizes Pseudo-3D for V-net improvement, adds conditional random field after generator and use original image as supplemental guidance. Results, using the BraTS-2018 dataset, show that 3D-vGAN outperforms classical segmentation models, including U-net, Gan, FCN and 3D V-net, reaching specificity over 99.8%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_14418 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field Jiang, Lan Zheng, Yuchao Yu, Miao Zhang, Haiqing Aladwani, Fatemah Perelli, Alessandro Image and Video Processing Computer Vision and Pattern Recognition 15-11 I.4.6; I.5.4 Accurate brain tumor segmentation remains a challenging task due to structural complexity and great individual differences of gliomas. Leveraging the pre-eminent detail resilience of CRF and spatial feature extraction capacity of V-net, we propose a multimodal 3D Volume Generative Adversarial Network (3D-vGAN) for precise segmentation. The model utilizes Pseudo-3D for V-net improvement, adds conditional random field after generator and use original image as supplemental guidance. Results, using the BraTS-2018 dataset, show that 3D-vGAN outperforms classical segmentation models, including U-net, Gan, FCN and 3D V-net, reaching specificity over 99.8%. |
| title | Multimodal 3D Brain Tumor Segmentation with Adversarial Training and Conditional Random Field |
| topic | Image and Video Processing Computer Vision and Pattern Recognition 15-11 I.4.6; I.5.4 |
| url | https://arxiv.org/abs/2411.14418 |