Advancing Brain Tumor Inpainting with Generative Models
Zhu, Ruizhi, Zhang, Xinru, Pang, Haowen, Xu, Chundan, Ye, Chuyang
–arXiv.org Artificial Intelligence
Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms, such as tissue segmentation and brain extraction algorithms, which may not effectively handle diseased images. We consider this a 3D inpainting task and investigate the adaptation of 2D inpainting methods to meet the requirements of 3D magnetic resonance imaging (MRI) data. Our contributions encompass potential modifications tailored to MRI-specific needs, and we conducted evaluations of multiple inpainting techniques using the BraTS2023 Inpainting datasets to assess their efficacy and limitations.
arXiv.org Artificial Intelligence
Feb-2-2024
- Country:
- Asia > China
- Europe > Germany
- Bavaria > Upper Bavaria > Munich (0.05)
- North America > United States
- New York > New York County > New York City (0.05)
- Genre:
- Research Report (0.85)
- Industry:
- Health & Medicine > Diagnostic Medicine > Imaging (1.00)
- Technology: