CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929391115698176 |
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| author | Zhang, Qimin Qi, Weiwei Zheng, Huili Shen, Xinyu |
| author_facet | Zhang, Qimin Qi, Weiwei Zheng, Huili Shen, Xinyu |
| contents | Accurately segmenting brain tumors from MRI scans is important for developing effective treatment plans and improving patient outcomes. This study introduces a new implementation of the Columbia-University-Net (CU-Net) architecture for brain tumor segmentation using the BraTS 2019 dataset. The CU-Net model has a symmetrical U-shaped structure and uses convolutional layers, max pooling, and upsampling operations to achieve high-resolution segmentation. Our CU-Net model achieved a Dice score of 82.41%, surpassing two other state-of-the-art models. This improvement in segmentation accuracy highlights the robustness and effectiveness of the model, which helps to accurately delineate tumor boundaries, which is crucial for surgical planning and radiation therapy, and ultimately has the potential to improve patient outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13113 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset Zhang, Qimin Qi, Weiwei Zheng, Huili Shen, Xinyu Computer Vision and Pattern Recognition Artificial Intelligence Neurons and Cognition Accurately segmenting brain tumors from MRI scans is important for developing effective treatment plans and improving patient outcomes. This study introduces a new implementation of the Columbia-University-Net (CU-Net) architecture for brain tumor segmentation using the BraTS 2019 dataset. The CU-Net model has a symmetrical U-shaped structure and uses convolutional layers, max pooling, and upsampling operations to achieve high-resolution segmentation. Our CU-Net model achieved a Dice score of 82.41%, surpassing two other state-of-the-art models. This improvement in segmentation accuracy highlights the robustness and effectiveness of the model, which helps to accurately delineate tumor boundaries, which is crucial for surgical planning and radiation therapy, and ultimately has the potential to improve patient outcomes. |
| title | CU-Net: a U-Net architecture for efficient brain-tumor segmentation on BraTS 2019 dataset |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Neurons and Cognition |
| url | https://arxiv.org/abs/2406.13113 |