CarveMix-RC：通过病灶感知合成增强解决脑转移瘤分割中的罕见类别不平衡问题
CarveMix-RC: Addressing Rare-Class Imbalance Through Lesion-Aware Synthetic Augmentation for Brain Metastasis Segmentation
浏览论文内容
中文总结 AI 辅助
针对脑转移瘤术后切除腔分割的罕见类别不平衡问题，提出基于nnU-Net的框架，结合RC加权损失、解剖一致增强、残差编码器与病灶感知后处理，在BraTS-MET 2026基准上显著提升RC分割性能。
中文摘要 AI 辅助
准确分割治疗后的脑转移瘤对于治疗规划、纵向疾病监测和治疗反应的定量评估至关重要。BraTS-MET 2026任务1挑战赛引入了一个临床相关的分割问题，涉及四个解剖学上不同的肿瘤亚区域：非增强肿瘤核心（NETC）、周围非增强FLAIR高信号（SNFH）、增强肿瘤（ET）和切除腔（RC）。其中，RC分割尤其具有挑战性，因为其患病率低、术后外观异质性强以及病灶级评估方案，导致传统分割网络在优化过程中优先考虑主要肿瘤类别。所提出的基于nnU-Net的框架通过四个互补组件明确解决RC分割问题：（i）RC加权Dice和交叉熵优化以缓解类别不平衡，（ii）解剖学一致的腔增强以增加术后腔外观的多样性，（iii）残差编码器架构以增强多尺度特征学习，以及（iv）病灶感知形态学后处理以抑制假阳性腔预测，同时保留解剖学上合理的结构。该框架在BraTS-MET 2026任务1在线验证基准上进行了评估。在评估的配置中，集成模型（残差编码器nnU-Net + nnU-Net + RC感知CarveMix）取得了最佳性能，对于ET、TC、WT和RC，病灶级Dice分数分别为0.732、0.752、0.708和0.575，相应的NSD分数分别为0.794、0.798、0.727和0.474。这些实验结果表明，集成RC感知优化、解剖学一致的增强和病灶感知后处理为改善治疗后脑转移瘤中罕见切除腔分割提供了一种有效策略。
英文摘要
Accurate segmentation of post-treatment brain metastases is essential for treatment planning, longitudinal disease monitoring, and quantitative assessment of therapeutic response. The BraTS-MET 2026 Task 1 challenge introduces a clinically relevant segmentation problem involving four anatomically distinct tumor subregions: non-enhancing tumor core (NETC), surrounding non-enhancing FLAIR hyperintensity (SNFH), enhancing tumor (ET), and the resection cavity (RC). Among these, RC segmentation is particularly challenging because of its low prevalence, heterogeneous postoperative appearance, and lesion-wise evaluation protocol, leading conventional segmentation networks to prioritize dominant tumor classes during optimization. The proposed nnU-Net-based framework explicitly addresses RC segmentation through four complementary components: (i) RC-weighted Dice and Cross-Entropy optimization to alleviate class imbalance, (ii) anatomically consistent cavity augmentation to increase the diversity of postoperative cavity appearances, (iii) a residual encoder architecture for enhanced multi-scale feature learning, and (iv) lesion-aware morphological post-processing to suppress false-positive cavity predictions while preserving anatomically plausible structures. The framework is evaluated on the BraTS-MET 2026 Task 1 online validation benchmark. Among the evaluated configurations, the ensemble model (Residual Encoder nnU-Net + nnU-Net + RC-aware CarveMix) achieves the best performance, with lesion-wise Dice scores of 0.732, 0.752, 0.708, and 0.575 and corresponding NSD scores of 0.794, 0.798, 0.727, and 0.474 for ET, TC, WT, and RC, respectively. These experimental results show that integrating RC-aware optimization, anatomically consistent augmentation, and lesion-aware post-processing provides an effective strategy for improving rare resection cavity segmentation in post-treatment brain metastases.
发表机构
- Old Dominion University（奥多明尼昂大学）
机构由 AI 辅助整理，请以论文原文为准。
