Advancing Brain Tumor Inpainting with Generative Models

• URL: http://arxiv.org/abs/2402.01509v1
• Date: Fri, 2 Feb 2024 15:43:51 GMT
• Title: Advancing Brain Tumor Inpainting with Generative Models
• Authors: Ruizhi Zhu, Xinru Zhang, Haowen Pang, Chundan Xu, Chuyang Ye
• Abstract summary: Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms. 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.
• Score: 0.7499722271664147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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