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Advancing Brain Tumor Inpainting with Generative Models

Ruizhi Zhu, Xinru Zhang, Haowen Pang, Chundan Xu, Chuyang Ye

TL;DR

This work addresses the challenge of analyzing brain tumors by treating the problem as 3D MRI inpainting to synthesize healthy brain tissue. It evaluates three MRI-focused approaches—pGAN, ResViT, and 3D Palette—each with MRI-tailored preprocessing and architectural adaptations, on the BraTS2023 Inpainting data. Results show ResViT yields finer detail but does not consistently outperform the diffusion- or GAN-based baselines in standard metrics, while 3D Palette demonstrates potential yet is hindered by slower inference and limited sample evaluation. The findings underscore the importance of 3D-specific design choices and potential benefits from model ensembles to improve fidelity and practicality for clinical workflows.

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.

Advancing Brain Tumor Inpainting with Generative Models

TL;DR

This work addresses the challenge of analyzing brain tumors by treating the problem as 3D MRI inpainting to synthesize healthy brain tissue. It evaluates three MRI-focused approaches—pGAN, ResViT, and 3D Palette—each with MRI-tailored preprocessing and architectural adaptations, on the BraTS2023 Inpainting data. Results show ResViT yields finer detail but does not consistently outperform the diffusion- or GAN-based baselines in standard metrics, while 3D Palette demonstrates potential yet is hindered by slower inference and limited sample evaluation. The findings underscore the importance of 3D-specific design choices and potential benefits from model ensembles to improve fidelity and practicality for clinical workflows.

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.
Paper Structure (15 sections, 2 equations, 2 figures, 2 tables)

This paper contains 15 sections, 2 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. pGAN
  4. Preprocessing
  5. Network
  6. ResViT
  7. Preprocessing
  8. Network
  9. 3D Palette
  10. Preprocessing
  11. Network
  12. Results
  13. Qualitative evaluation
  14. Quantitative evaluation
  15. Discussion

Figures (2)

  • Figure 1: Comparison of inpainting results for regions without tumors.
  • Figure 2: Comparison of inpainting results for regions with tumors.