Gliomas are the most common malignant brain tumors in adults and are among the most lethal. Despite aggressive treatment, the median survival rate is less than 15 months. Accurate multiparametric MRI (mpMRI) tumor segmentation is critical for surgical planning, radiotherapy, and disease monitoring. While deep learning models have improved the accuracy of automated segmentation, large-scale pre-trained models generalize poorly and often underperform, producing systematic errors such as false positives, label swaps, and slice discontinuities in slices. These limitations are further compounded by unequal access to GPU resources and the growing environmental cost of large-scale model training. In this work, we propose adaptive post-processing techniques to refine the quality of glioma segmentations produced by large-scale pretrained models developed for various types of tumors. We demonstrated the techniques in multiple BraTS 2025 segmentation challenge tasks, with the ranking metric improving by 14.9 % for the sub-Saharan Africa challenge and 0.9% for the adult glioma challenge. This approach promotes a shift in brain tumor segmentation research from increasingly complex model architectures to efficient, clinically aligned post-processing strategies that are precise, computationally fair, and sustainable.

翻译：胶质瘤是成人中最常见的恶性脑肿瘤，且致死率极高。尽管采取积极治疗，患者中位生存期仍不足15个月。精准的多参数磁共振成像（mpMRI）肿瘤分割对于手术规划、放射治疗及疾病监测至关重要。虽然深度学习模型提升了自动分割的准确性，但大规模预训练模型泛化能力不佳且常表现欠佳，会产生假阳性、标签交换及切片不连续等系统性错误。这些局限性因GPU资源获取不平等及大规模模型训练带来的环境成本增长而进一步加剧。本研究提出自适应后处理技术，以改善针对多种肿瘤类型开发的大规模预训练模型所生成的胶质瘤分割质量。我们在多项BraTS 2025分割挑战任务中验证了该技术：在撒哈拉以南非洲挑战中排序指标提升14.9%，在成人胶质瘤挑战中提升0.9%。该方法推动脑肿瘤分割研究从日益复杂的模型架构转向高效、临床适配的后处理策略——这些策略兼具精确性、计算公平性与可持续性。