Advancing Brain Tumor Inpainting with Generative Models
This work addresses limitations in general-purpose algorithms for brain tumor analysis, but it appears incremental as it focuses on adapting existing methods to a specific domain.
The paper tackled the problem of synthesizing healthy brain scans from diseased ones by adapting 2D inpainting methods to 3D MRI data, evaluating multiple techniques on the BraTS2023 Inpainting datasets to assess their efficacy and limitations.
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.