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.

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

Surgical data science is devoted to enhancing the quality, safety, and efficacy of interventional healthcare. While the use of powerful machine learning algorithms is becoming the standard approach for surgical data science, the underlying end-to-end task models directly infer high-level concepts (e.g., surgical phase or skill) from low-level observations (e.g., endoscopic video). This end-to-end nature of contemporary approaches makes the models vulnerable to non-causal relationships in the data and requires the re-development of all components if new surgical data science tasks are to be solved. The digital twin (DT) paradigm, an approach to building and maintaining computational representations of real-world scenarios, offers a framework for separating low-level processing from high-level inference. In surgical data science, the DT paradigm would allow for the development of generalist surgical data science approaches on top of the universal DT representation, deferring DT model building to low-level computer vision algorithms. In this latter effort of DT model creation, geometric scene understanding plays a central role in building and updating the digital model. In this work, we visit existing geometric representations, geometric scene understanding tasks, and successful applications for building primitive DT frameworks. Although the development of advanced methods is still hindered in surgical data science by the lack of annotations, the complexity and limited observability of the scene, emerging works on synthetic data generation, sim-to-real generalization, and foundation models offer new directions for overcoming these challenges and advancing the DT paradigm. 

Digital twins as a unifying framework for surgical data science: the enabling role of geometric scene understanding

Inspired by the ”What Matters in Unsupervised Optical Flow” study, the goal of this work is to evaluate the performance of the ARFlow architecture for unsupervised optical flow in the context of tracking keypoints in laparoscopic videos. This assessment could provide insight into the applicability of ARFlow and similar architectures for this particular application, as well as their strengths and limitations. To do so, we use the SurgT challenge’s dataset and metrics to evaluate the tracker’s accuracy and robustness and its relationship with distinct network components. Our results corroborate some of the findings reported by Jonschkowski et al. However, certain components demonstrate a distinct behavior, possibly indicating underlying issues, namely intrinsic to the application, that impact overall performance and which may have to be addressed in the context of soft-tissue trackers. These results point to potential bottlenecks and areas where future work may target on.

Evaluating unsupervised optical flow for keypoint tracking in laparoscopic videos

Tracking the surgical site in surgical images is a method that can help doctors. Therefore, we proposed a tracker model that learns by self-supervised learning for tissue organization in surgical images. We constructed a model based on unsupervised deep tracking (UDT) as a base line Our proposed model incorporates forward and backward prediction in conjunction with a Siamese network, a feature that sets it apart as a robust tracking solution. Through a series of extensive experiments, we established the effectiveness of our proposed model in comparison with existing models in the field. Our model achieved an Expected Average Overlap (EAO) score of 0.405, demonstrating significant improvements over existing models: TransT (0.274), KIT (0.223), SRV (0.293), and MEDCVR (0.302), with respective score improvements of 0.131, 0.182, 0.112, and 0.103. These results confirm that our proposed model can be helpful in performing effective tracking in surgical videos. 

Tracker Learning Surgical Images By Self-Supervised Learning: An Enhanced Unsupervised Deep Tracking Approach

3D scene reconstruction from stereo endoscopic video data is crucial for advancing surgical interventions. In this work, we present an online framework for online, dense 3D scene reconstruction and tracking, aimed at enhancing surgical scene understanding and assisting interventions. Our method dynamically extends a canonical scene representation using Gaussian splatting, while modeling tissue deformations through a sparse set of control points. We introduce an efficient online fitting algorithm that optimizes the scene parameters, enabling consistent tracking and accurate reconstruction. Through experiments on the StereoMIS dataset, we demonstrate the effectiveness of our approach, outperforming state-of-the-art tracking methods and achieving comparable performance to offline reconstruction techniques. Our work enables various downstream applications thus contributing to advancing the capabilities of surgical assistance systems. 

Online 3D reconstruction and dense tracking in endoscopic videos

This paper introduces the"SurgT: Surgical Tracking"challenge which was organised in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022). There were two purposes for the creation of this challenge: (1) the establishment of the first standardised benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, have been provided. The participants were tasked with the development of algorithms to track a bounding box on stereo endoscopic videos. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge and are now available online. The teams were ranked according to their Expected Average Overlap (EAO) score, which is a weighted average of the Intersection over Union (IoU) scores. The performance evaluation study verifies the efficacy of unsupervised deep learning algorithms in tracking soft-tissue. The best-performing method achieved an EAO score of 0.583 in the test subset. The dataset and benchmarking tool created for this challenge have been made publicly available. This challenge is expected to contribute to the development of autonomous robotic surgery and other digital surgical technologies.

pdf/surgt-challenge-benchmark-of-soft-tissue-trackers-for-qwpif4y4.pdf

SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery

This paper introduces the SurgT MICCAI 2022 challenge and its ﬁrst results. There were two purposes for the creation of this challenge: (1) the establishment of the ﬁrst standardised benchmark for the research community to assess soft-tissue trackers; and (2) to encourage the development of unsupervised deep learning methods, given the lack of annotated data in surgery. A dataset of 157 stereo endoscopic videos from 20 clinical cases, along with stereo camera calibration parameters, are provided. The participants were tasked with the development of algorithms to track a bounding box on each stereo endoscopic video. At the end of the challenge, the developed methods were assessed on a previously hidden test subset. This assessment uses benchmarking metrics that were purposely developed for this challenge and are now available online. The teams were ranked according to their Expected Average Overlap (EAO) score, which is a weighted average of Intersection over Union (IoU) scores. The top team achieved an EAO score of 0.583 in the test subset. Tracking soft-tissue using unsupervised algorithms was found to be achievable. The dataset and benchmarking tool have been successfully created and made publicly available online. This challenge is expected to contribute to the development of autonomous robotic surgery, and other digital surgical technologies

SurgT: Soft-Tissue Tracking for Robotic Surgery, Benchmark and Challenge

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SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery

SurgT: Soft-Tissue Tracking for Robotic Surgery, Benchmark and Challenge