用深度特征和拓扑先验构建结肠镜全程拓扑地图
Topological SLAM in colonoscopies leveraging deep features and topological priors
- 结合度量SLAM与深度定位网络，利用拓扑先验关联远时间跨度的图像
- 在真实结肠镜数据中成功构建完整结肠拓扑地图，覆盖多个子图
- 适合医疗导航与内窥镜路径重建场景，尤其适用于动态变形器官
我们提出ColonSLAM系统，将经典多地图度量SLAM与深度特征及拓扑先验结合，构建整个结肠的拓扑地图。原有的SLAM管道可生成代表结肠短时视频片段的独立度量子图，但由于肠道形变及SIFT描述子在医学领域的性能有限，无法有效合并共视子图。ColonSLAM通过拓扑先验引导，融合一个训练用于判断两幅图像是否来自同一位置的深度定位网络，以及基于Transformer的匹配网络进行软验证，能够在探索过程中关联远时间间隔的子图，将相同结肠位置的子图归为节点，构建比现有方法更复杂的地图。我们在Endomapper数据集上验证了该方法的有效性，展示了其在真实人类结肠镜检查中生成全结肠地图的潜力。代码与模型已开源：https://github.com/endomapper/ColonSLAM。
原文摘要 · Abstract (English)
We introduce ColonSLAM, a system that combines classical multiple-map metric SLAM with deep features and topological priors to create topological maps of the whole colon. The SLAM pipeline by itself is able to create disconnected individual metric submaps representing locations from short video subsections of the colon, but is not able to merge covisible submaps due to deformations and the limited performance of the SIFT descriptor in the medical domain. ColonSLAM is guided by topological priors and combines a deep localization network trained to distinguish if two images come from the same place or not and the soft verification of a transformer-based matching network, being able to relate far-in-time submaps during an exploration, grouping them in nodes imaging the same colon place, building more complex maps than any other approach in the literature. We demonstrate our approach in the Endomapper dataset, showing its potential for producing maps of the whole colon in real human explorations. Code and models are available at: https://github.com/endomapper/ColonSLAM.
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