Quantifying performance of methods for tracking and mapping tissue in endoscopic environments is essential for enabling image guidance and automation of medical interventions and surgery. Datasets developed so far either use rigid environments, visible markers, or require annotators to label salient points in videos after collection. These are respectively: not general, visible to algorithms, or costly and error-prone. We introduce a novel labeling methodology along with a dataset that uses said methodology, Surgical Tattoos in Infrared (STIR). STIR has labels that are persistent but invisible to visible spectrum algorithms. This is done by labelling tissue points with IR-fluorescent dye, indocyanine green (ICG), and then collecting visible light video clips. STIR comprises hundreds of stereo video clips in both in vivo and ex vivo scenes with start and end points labelled in the IR spectrum. With over 3,000 labelled points, STIR will help to quantify and enable better analysis of tracking and mapping methods. After introducing STIR, we analyze multiple different frame-based tracking methods on STIR using both 3D and 2D endpoint error and accuracy metrics. STIR is available at https://dx.doi.org/10.21227/w8g4-g548

中文翻译：

---

红外手术纹身：用于量化组织跟踪和绘图的数据集


量化内窥镜环境中跟踪和绘制组织的方法的性能对于实现医疗干预和手术的图像引导和自动化至关重要。迄今为止开发的数据集要么使用严格的环境、可见的标记，要么要求注释者在收集后标记视频中的显着点。这些分别是：不通用、算法可见、成本高昂且容易出错。我们引入了一种新颖的标记方法以及使用该方法的数据集，即红外纹身手术（STIR）。 STIR 具有持久性但对可见光谱算法不可见的标签。这是通过用红外荧光染料吲哚菁绿 (ICG) 标记组织点，然后收集可见光视频剪辑来完成的。 STIR 包含数百个体内和离体场景的立体视频剪辑，并在红外光谱中标记了起点和终点。 STIR 拥有 3,000 多个标记点，将有助于量化并更好地分析跟踪和绘图方法。介绍 STIR 后，我们使用 3D 和 2D 端点误差和准确度指标来分析 STIR 上多种不同的基于帧的跟踪方法。 STIR 可在 https://dx.doi.org/10.21227/w8g4-g548 获取