ArticleInternational journal of computer assisted radiology and surgery2020
Object extraction via deep learning-based marker-free tracking framework of surgical instruments for laparoscope-holder robots.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 46 citations in OpenAlex.
- Efficiency in image-guided robotic and conventional camera steering: a prospective randomized controlled trial.Surgical endoscopy · 2022Trial
- Artificial intelligence-based model for identifying anatomical and surgical structures in the videos of laparoscopic left hemicolectomy.Journal of robotic surgery · 2026Article
- Integration of Trackerless Surface Reconstruction-Based Surgical Navigation With Exoscopic Trans-Mastoid Surgery.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Article
- Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.Frontiers in digital health · 2026Review
- Use of artificial intelligence in the analysis of digital videos of invasive surgical procedures: scoping review.BJS open · 2025Article
- Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review.Frontiers in surgery · 2025Review
- Real-Time Tool Localization for Laparoscopic Surgery Using Convolutional Neural Network.Sensors (Basel, Switzerland) · 2024Article
- Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction.JAMA otolaryngology-- head & neck surgery · 2024Article
- 3D Autonomous Surgeon's Hand Movement Assessment Using a Cascaded Fuzzy Supervisor in Multi-Thread Video Processing.Sensors (Basel, Switzerland) · 2023Article
- Development and Validation of a Model for Laparoscopic Colorectal Surgical Instrument Recognition Using Convolutional Neural Network-Based Instance Segmentation and Videos of Laparoscopic Procedures.JAMA network open · 2022Article
- Limited generalizability of single deep neural network for surgical instrument segmentation in different surgical environments.Scientific reports · 2022Article
- Artificial intelligence-based computer vision in surgery: Recent advances and future perspectives.Annals of gastroenterological surgery · 2022Review
- Pose estimation of a markerless fiber bundle for endoscopic optical biopsy.Journal of medical imaging (Bellingham, Wash.) · 2021Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
purposeThe surgical instrument tracking framework, especially the marker-free surgical instrument tracking framework, is the key to visual servoing which is applied to achieve active control for laparoscope-holder robots. This paper presented a marker-free surgical instrument tracking framework based on object extraction via deep learning (DL).
methodsThe surgical instrument joint was defined as the tracking point. Using DL, a segmentation model was trained to extract the end-effector and shaft portions of the surgical instrument in real time. The extracted object was transformed into a distance image by Euclidean Distance Transformation. Next, the points with the maximal pixel value in the two portions were defined as their central points, respectively, and the intersection point of the line connecting the two central points and the plane connecting the two portions was determined as the tracking point. Finally, the object could be fast extracted using the masking method, and the tracking point was fast located frame-by-frame in a laparoscopic video to achieve tracking of surgical instrument. The proposed object extraction via a DL-based marker-free tracking framework was compared with a marker-free tracking-by-detection framework via DL.
resultsUsing seven in vivo laparoscopic videos for experiments, the mean tracking success rate was 100%. The mean tracking accuracy was (3.9 ± 2.4, 4.0 ± 2.5) pixels measured in u and v coordinates of a frame, and the mean tracking speed was 15 fps. Compared to the reported mean tracking accuracy of a marker-free tracking-by-detection framework via DL, the mean tracking accuracy of our proposed tracking framework was improved by 37% and 23%, respectively.
conclusionAccurate and fast tracking of marker-free surgical instruments could be achieved in in vivo laparoscopic videos by using our proposed object extraction via DL-based marker-free tracking framework. This work provided important guiding significance for the application of laparoscope-holder robots in laparoscopic surgeries.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.