Abstract
Magnetic Resonance Imaging (MRI) is the primary imaging modality used in the diagnosis, assessment, and treatment planning for brain pathologies. However, most automated MRI analysis tools, such as segmentation and registration pipelines, are optimized for healthy anatomies and often fail when confronted with large lesions such as tumors. To overcome this, image inpainting techniques aim to locally synthesize healthy brain tissues in tumor regions, enabling the reliable application of general-purpose tools. In this work, we systematically evaluate state-of-the-art inpainting models and observe a saturation in their standalone performance. In response, we introduce a methodology combining model ensembling with efficient post-processing strategies such as median filtering, histogram matching, and pixel averaging. Further anatomical refinement is achieved via a lightweight U-Net enhancement stage. Comprehensive evaluation demonstrates that our proposed pipeline improves the anatomical plausibility and visual fidelity of inpainted regions, yielding higher accuracy and more robust outcomes than individual baseline models. By combining established models with targeted post-processing, we achieve improved and more accessible inpainting outcomes, supporting broader clinical deployment and sustainable, resource-conscious research. Our 2025 BraTS inpainting docker is available at this https URL.
Abstract (translated)
磁共振成像（MRI）是用于诊断、评估和治疗规划脑部疾病的主要影像学方法。然而，大多数自动化的MRI分析工具，如分割和配准管道，都是为正常解剖结构优化的，在遇到大型病变（例如肿瘤）时常常失效。为了克服这一问题，图像修复技术旨在局部合成肿瘤区域内的健康脑组织，从而使通用工具能够可靠地应用。 在本研究中，我们系统性地评估了当前最先进的修复模型，并观察到它们独立性能上的饱和现象。作为回应，我们提出了一种结合模型集成与有效后处理策略（如中值滤波、直方图匹配和像素平均）的方法。通过一个轻量级的U-Net增强阶段进一步实现解剖学精炼。全面评估表明，我们的提议管道提高了修复区域的解剖合理性及视觉真实性，并且相较于单独的基础模型产生了更高的准确性和更稳健的结果。 通过结合现有的模型与针对性的后处理技术，我们实现了改进和更具可访问性的图像修复结果，支持更广泛的临床部署以及可持续、资源节约型的研究。我们的2025 BraTS 图像修复Docker在[此处](https://此链接应为实际提供的URL)可用。
URL
https://arxiv.org/abs/2510.15282
