An Ensemble Approach for Brain Tumor Segmentation and Synthesis
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arXiv
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| Format: | Preprint |
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2024
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| author | Rivera, Juampablo E. Heras Chopra, Agamdeep S. Ren, Tianyi Oswal, Hitender Pan, Yutong Sordo, Zineb Walters, Sophie Henry, William Mohammadi, Hooman Olson, Riley Rezayaraghi, Fargol Lam, Tyson Jaikanth, Akshay Kancharla, Pavan Ruzevick, Jacob Ushizima, Daniela Kurt, Mehmet |
| author_facet | Rivera, Juampablo E. Heras Chopra, Agamdeep S. Ren, Tianyi Oswal, Hitender Pan, Yutong Sordo, Zineb Walters, Sophie Henry, William Mohammadi, Hooman Olson, Riley Rezayaraghi, Fargol Lam, Tyson Jaikanth, Akshay Kancharla, Pavan Ruzevick, Jacob Ushizima, Daniela Kurt, Mehmet |
| contents | The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17617 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Ensemble Approach for Brain Tumor Segmentation and Synthesis Rivera, Juampablo E. Heras Chopra, Agamdeep S. Ren, Tianyi Oswal, Hitender Pan, Yutong Sordo, Zineb Walters, Sophie Henry, William Mohammadi, Hooman Olson, Riley Rezayaraghi, Fargol Lam, Tyson Jaikanth, Akshay Kancharla, Pavan Ruzevick, Jacob Ushizima, Daniela Kurt, Mehmet Image and Video Processing Computer Vision and Pattern Recognition The integration of machine learning in magnetic resonance imaging (MRI), specifically in neuroimaging, is proving to be incredibly effective, leading to better diagnostic accuracy, accelerated image analysis, and data-driven insights, which can potentially transform patient care. Deep learning models utilize multiple layers of processing to capture intricate details of complex data, which can then be used on a variety of tasks, including brain tumor classification, segmentation, image synthesis, and registration. Previous research demonstrates high accuracy in tumor segmentation using various model architectures, including nn-UNet and Swin-UNet. U-Mamba, which uses state space modeling, also achieves high accuracy in medical image segmentation. To leverage these models, we propose a deep learning framework that ensembles these state-of-the-art architectures to achieve accurate segmentation and produce finely synthesized images. |
| title | An Ensemble Approach for Brain Tumor Segmentation and Synthesis |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.17617 |