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

翻译：量化内窥镜环境中组织追踪与映射方法的性能，对于实现医学干预和手术的图像引导及自动化至关重要。目前已有的数据集要么使用刚体环境、可见标记，要么需要标注员在采集后对视频中的显著点进行标记。这些方法分别存在以下问题：不具有通用性、对算法可见、或成本高昂且易出错。本文提出了一种新型标注方法，并基于该方法构建了数据集——红外手术纹身（STIR）。STIR的标签具有持久性，但对可见光谱算法不可见。我们通过使用红外荧光染料吲哚青绿（ICG）标记组织点，随后采集可见光视频片段来实现这一目标。STIR包含数百个离体和活体场景下的立体视频片段，并在红外光谱中标注了起止点。凭借超过3000个标注点，STIR将有助于量化并更好地分析追踪与映射方法。在介绍STIR后，我们使用3D和2D端点误差及精度指标，分析了多种基于帧的追踪方法在STIR上的性能。STIR数据集访问地址为：https://dx.doi.org/10.21227/w8g4-g548