用解剖结构增强病灶分割，提升医学图像分析精度
GRASPing Anatomy to Improve Pathology Segmentation
- 通过伪标签和特征对齐融合解剖信息
- 在两个PET/CT数据集上均显著提升分割性能
- 无需重训练解剖模型，适配多种分割架构
放射科医生依赖解剖知识准确勾画病灶，但现有深度学习方法多基于纯模式识别，忽略病灶发育的解剖背景。为缩小这一差距，我们提出GRASP（Guided Representation Alignment for the Segmentation of Pathologies），一种模块化即插即用框架，通过伪标签融合与特征对齐，利用现有解剖分割模型提升病灶分割模型性能。与以往依赖辅助训练获取解剖知识的方法不同，GRASP可无缝集成至标准病灶优化流程，无需重新训练解剖组件。我们在两个PET/CT数据集上评估GRASP，进行系统消融实验并分析其内部机制。结果表明，GRASP在多个评价指标和不同架构下均表现最优。其双策略解剖注入机制——将解剖伪标签作为输入通道，并结合Transformer引导的解剖特征融合——有效融入了解剖上下文信息。
原文摘要 · Abstract (English)
Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical context in which pathologies develop. To narrow this gap, we introduce GRASP (Guided Representation Alignment for the Segmentation of Pathologies), a modular plug-and-play framework that enhances pathology segmentation models by leveraging existing anatomy segmentation models through pseudolabel integration and feature alignment. Unlike previous approaches that obtain anatomical knowledge via auxiliary training, GRASP integrates into standard pathology optimization regimes without retraining anatomical components. We evaluate GRASP on two PET/CT datasets, conduct systematic ablation studies, and investigate the framework's inner workings. We find that GRASP consistently achieves top rankings across multiple evaluation metrics and diverse architectures. The framework's dual anatomy injection strategy, combining anatomical pseudo-labels as input channels with transformer-guided anatomical feature fusion, effectively incorporates anatomical context.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。