仅用后处理技术提升脑胶质瘤分割模型性能，显著改善精度与可及性。
Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques
- 采用自适应后处理优化预训练模型输出，修复误检与切片断裂问题。
- 在BraTS 2025挑战中，非洲赛区排名提升14.9%，成人胶质瘤赛题提升0.9%。
- 方法轻量高效，适合资源受限场景，推动可持续医疗AI发展。
胶质瘤是成人最常见的恶性脑肿瘤，尽管治疗积极，中位生存期仍不足15个月。准确的多参数MRI（mpMRI）肿瘤分割对术前规划、放疗和疾病监测至关重要。尽管深度学习提升了自动化分割的准确性，大规模预训练模型泛化能力差，常出现假阳性、标签混淆和切片不连续等系统性错误。这一问题因GPU资源获取不均及大规模训练带来的环境成本而加剧。本文提出自适应后处理技术，用于优化针对多种肿瘤类型训练的大规模预训练模型输出。我们在BraTS 2025多个分割挑战任务中验证该方法，亚撒哈拉非洲赛区排名提升14.9%，成人胶质瘤赛区提升0.9%。该方法推动脑肿瘤分割研究从复杂模型架构转向高效、临床对齐的后处理策略，兼具高精度、计算公平性和可持续性。
原文摘要 · Abstract (English)
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.
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