提出新框架，同时精准检测脑小血管病的两种病灶。
A Unified Framework for Joint Detection of Lacunes and Enlarged Perivascular Spaces
- 用解耦结构分离稀疏病灶与密集背景，提升检测精度。
- 在VALDO数据集上，腔隙检测准确率71.1%，显著优于冠军方案。
- 适合脑影像分析、神经疾病研究者使用，尤其关注多病灶联合诊断。
脑小血管病（CSVD）标志物——扩大周围间隙（EPVS）和腔隙——因影像学表现相似，在医学图像分析中面临挑战。标准分割网络在处理这两种差异显著的目标时，易受特征干扰且存在极端类别不平衡问题。为此，我们提出一种形态解耦框架：零初始化门控跨任务注意力利用密集的EPVS上下文引导稀疏腔隙检测；通过融合互斥约束与中心线Dice损失的混合监督策略，强制生物学与拓扑一致性；最后引入解剖信息推理校准机制，动态抑制基于组织语义的假阳性。在VALDO 2021数据集（N=40）上的五折交叉验证显示，该方法达到顶尖性能，尤其在腔隙检测精度（71.1%，p=0.01）和F1分数（62.6%，p=0.03）上显著超越任务优胜者。外部EPAD队列（N=1762）评估进一步验证了模型在大规模人群研究中的鲁棒性。代码将在录用后公开。
原文摘要 · Abstract (English)
Cerebral small vessel disease (CSVD) markers, specifically enlarged perivascular spaces (EPVS) and lacunae, present a unique challenge in medical image analysis due to their radiological mimicry. Standard segmentation networks struggle with feature interference and extreme class imbalance when handling these divergent targets simultaneously. To address these issues, we propose a morphology-decoupled framework where Zero-Initialized Gated Cross-Task Attention exploits dense EPVS context to guide sparse lacune detection. Furthermore, biological and topological consistency are enforced via a mixed-supervision strategy integrating Mutual Exclusion and Centerline Dice losses. Finally, we introduce an Anatomically-Informed Inference Calibration mechanism to dynamically suppress false positives based on tissue semantics. Extensive 5-folds cross-validation on the VALDO 2021 dataset (N=40) demonstrates state-of-the-art performance, notably surpassing task winners in lacunae detection precision (71.1%, p=0.01) and F1-score (62.6%, p=0.03). Furthermore, evaluation on the external EPAD cohort (N=1762) confirms the model's robustness for large-scale population studies. Code will be released upon acceptance.
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。