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

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...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2024-07, Vol.43 (7), p.2634-2645
Main Authors: Schmidt, Adam, Mohareri, Omid, DiMaio, Simon P., Salcudean, Septimiu E.
Format: Article
Language:English
Subjects:
Algorithms
Animals
Automation
Clips
Datasets
Deformable tracking
endoscopic datasets
Fluorescent dyes
Fluorescent indicators
Humans
Image Processing, Computer-Assisted - methods
In vivo
Indocyanine Green
Infrared Rays
Infrared tracking
Ink
Labeling
Labelling
Labels
Mapping
Needles
simultaneous localization and mapping (SLAM)
Surgery
Surgery, Computer-Assisted - methods
Tattooing - methods
Tattoos
tissue tracking
Tracking
Video Recording - methods
Videos
Visible spectrum
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Summary: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
ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2024.3372828