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-flourescent 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
this https URL

为了实现图像引导和医疗介入和手术自动化，对于跟踪和映射组织方法的性能进行量化是至关重要的。我们引入了一种新的标注方法和一个使用该方法的数据集，称为 Surgical Tattoos in Infrared (STIR)。STIR 使用持久但对可见光谱算法不可见的标签，通过在组织点上标记 IR 荧光染料，indocyanine green (ICG)，然后收集可见光视频剪辑。STIR 包括数百个立体视频剪辑，涵盖了体内和体外场景，在 IR 谱中标记了起始点和终止点。通过引入 STIR，我们使用 3D 和 2D 端点误差和准确性指标分析了多种基于帧的跟踪方法。