用掩码引导追踪乳腺影像病灶，提升随访分析准确性
MammoTracker: Mask-Guided Lesion Tracking in Temporal Mammograms
- 分粗到细三阶段，结合全局与局部搜索定位病灶
- 平均重叠率0.455，准确率0.509，比基线高8%
- 适用于乳腺癌随访研究者和医学AI开发者
在时间序列乳腺钼靶图像中精准追踪病灶对早期诊断和监测乳腺癌进展至关重要。然而，自动识别不同检查间的病灶对应关系仍是计算机辅助诊断（CAD）系统的难点。本文提出MammoTracker，一种基于掩码引导的病灶追踪框架，实现连续检查中病灶的自动化定位。方法采用粗到细策略，包含全局搜索、局部搜索与评分优化三个模块。为支持大规模训练与评估，我们构建了一个新数据集，从公开的EMBED乳腺钼靶数据集中选取730例肿块与钙化病例，提供经标注的前次检查信息，共生成超过20000对病灶，是目前最大规模的时间性病灶追踪资源。实验表明，MammoTracker达到0.455的平均重叠率和0.509的准确率，相比基线模型提升8%，展现出增强CAD病灶进展分析的潜力。数据集将公开于https://gitlab.oit.duke.edu/railabs/LoGroup/mammotracker。
原文摘要 · Abstract (English)
Accurate lesion tracking in temporal mammograms is essential for monitoring breast cancer progression and facilitating early diagnosis. However, automated lesion correspondence across exams remains a challenges in computer-aided diagnosis (CAD) systems, limiting their effectiveness. We propose MammoTracker, a mask-guided lesion tracking framework that automates lesion localization across consecutively exams. Our approach follows a coarse-to-fine strategy incorporating three key modules: global search, local search, and score refinement. To support large-scale training and evaluation, we introduce a new dataset with curated prior-exam annotations for 730 mass and calcification cases from the public EMBED mammogram dataset, yielding over 20000 lesion pairs, making it the largest known resource for temporal lesion tracking in mammograms. Experimental results demonstrate that MammoTracker achieves 0.455 average overlap and 0.509 accuracy, surpassing baseline models by 8%, highlighting its potential to enhance CAD-based lesion progression analysis. Our dataset will be available at https://gitlab.oit.duke.edu/railabs/LoGroup/mammotracker.
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