零样本实现3D全身体积影像中肿瘤的精准追踪与分割
LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
- 基于2.3万例标注数据与合成纵向数据，构建端到端4D追踪框架
- 分割性能提升近10个Dice分数，达到人类水平，追踪准确率领先
- 首个开源合成4D数据集与模型，助力医学影像智能分析发展
本文提出LesionLocator，一个用于3D医学影像中零样本纵向病灶追踪与分割的框架，首次实现端到端4D追踪并支持密集空间提示。模型基于包含23,262例标注医学扫描及多种病灶类型的合成纵向数据，显著提升对真实世界医学影像挑战的泛化能力，并缓解纵向数据稀缺问题。LesionLocator在病灶分割上超越所有现有可提示模型近10个Dice分数，达到人类水平，在病灶追踪任务中表现最优，兼具高检索与分割精度。该工作不仅树立了通用可提示病灶分割与自动纵向追踪的新基准，还首次公开了合成4D数据集与模型，推动医学影像领域发展。代码已开源。
原文摘要 · Abstract (English)
In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capable of 4D tracking with dense spatial prompts. Our model leverages an extensive dataset of 23,262 annotated medical scans, as well as synthesized longitudinal data across diverse lesion types. The diversity and scale of our dataset significantly enhances model generalizability to real-world medical imaging challenges and addresses key limitations in longitudinal data availability. LesionLocator outperforms all existing promptable models in lesion segmentation by nearly 10 dice points, reaching human-level performance, and achieves state-of-the-art results in lesion tracking, with superior lesion retrieval and segmentation accuracy. LesionLocator not only sets a new benchmark in universal promptable lesion segmentation and automated longitudinal lesion tracking but also provides the first open-access solution of its kind, releasing our synthetic 4D dataset and model to the community, empowering future advancements in medical imaging. Code is available at: www.github.com/MIC-DKFZ/LesionLocator
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