Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping

TL;DR

This paper introduces STIR, a novel infrared-visible dataset with labeled tissue points using IR-fluorescent dye, to improve tissue tracking and mapping in endoscopic surgery, addressing limitations of previous datasets.

Contribution

The paper presents a new labeling methodology and a comprehensive dataset, STIR, for tissue tracking that uses IR-visible labels, enabling more accurate and persistent tracking in surgical environments.

Findings

Analyzed multiple frame-based tracking methods on STIR

Demonstrated the utility of IR labels for tissue tracking

Provided a publicly available dataset for future research

Abstract

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