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Real-Time Segmentation of Non-rigid Surgical Tools Based on Deep Learning and Tracking
[chapter]
2017
Lecture Notes in Computer Science
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Real-time tool segmentation is an essential component in computer-assisted surgical systems. ...
We propose a novel real-time automatic method based on Fully Convolutional Networks (FCN) and optical flow tracking. ...
Ebner and S. Nousias for the ground truth of FetalFlexTool and E. Maneas for preparing setup with an ex vivo placenta. ...
doi:10.1007/978-3-319-54057-3_8
fatcat:gtya6pjshvhvtbveyvyf6htnv4
A Review on Deep Learning in Minimally Invasive Surgery
2021
IEEE Access
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Real-time tracking of the surgical tools is addressed in [36] . ...
[49] present a weakly supervised framework for surgical tools tracking and segmentation based on a hybrid sensor system that integrates electromagnetic tracking with processing of visual data. ...
In 2004, he was given a permanent position on the research support staff at the University of Malaga. ...
doi:10.1109/access.2021.3068852
fatcat:gfpghqfptzdktlody5z263cdju
ToolNet: Holistically-nested real-time segmentation of robotic surgical tools
2017
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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We propose two novel deep learning architectures for automatic segmentation of non-rigid surgical instruments. ...
Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and of critical importance in robotic surgical data science. ...
The authors would like to thank NVIDIA Corporation for the donated GeForce GTX TITAN X GPU and all the members of the GIFT-Surg project for the always useful discussions. ...
doi:10.1109/iros.2017.8206462
dblp:conf/iros/Garcia-Peraza-Herrera17
fatcat:3xelwt5fubhpxgpjf6qahbto2q
Artificial Intelligence in Surgery
[article]
2019
arXiv
pre-print
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In this article, the recent successful and influential applications of AI in surgery are reviewed from pre-operative planning and intra-operative guidance to the integration of surgical robots. ...
Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation and robotic intervention. ...
These methods have different advantages and disadvantages. In the context of deep learning based surgical instrument tracking, the proposed methods were built on the tracking by detection [84, 85] . ...
arXiv:2001.00627v1
fatcat:dywtv6v36rgf3fummidyluy3zi
Front Matter: Volume 10576
2018
Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
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-35]
Machine learning-based colon deformation estimation method for colonoscope tracking
[10576-36]
CARDIAC AND LUNG IMAGING AND TRACKING
10576 1A
A real-time system for prosthetic valve tracking ...
[10576-43]
10576 1H
A system for automatic monitoring of surgical instruments and dynamic non-rigid surface
deformations in breast cancer surgery [10576-44]
10576 1I
Intraoperative deformation ...
doi:10.1117/12.2323924
fatcat:hva4ny4ftbe2voqifpbt5wewky
Front Matter: Volume 10135
2017
Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling
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A unique citation identifier (CID) number is assigned to each article at the time of publication. ...
Utilization of CIDs allows articles to be fully citable as soon as they are published online, and connects the same identifier to all online and print versions of the publication. ...
10135 1F
Optimization of real-time rigid registration motion compensation for prostate biopsies using
2D/3D ultrasound [10135-50]
Part Two
SESSION 11
ANATOMICAL MEASUREMENT AND RESPIRATORY TRACKING ...
doi:10.1117/12.2277134
dblp:conf/miigp/X17
fatcat:inrmsopsvfd47nfo35fhucwcmq
HMD-EgoPose: Head-Mounted Display-Based Egocentric Marker-Less Tool and Hand Pose Estimation for Augmented Surgical Guidance
[article]
2022
arXiv
pre-print
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HMD-EgoPose outperformed current state-of-the-art approaches on a benchmark dataset for surgical tool pose estimation, achieving an average tool 3D vertex error of 11.0 mm on real data and furthering the ...
Our framework utilized an efficient convolutional neural network (CNN) backbone for multi-scale feature extraction and a set of subnetworks to jointly learn the 6DoF pose representation of the rigid surgical ...
learning-based [13] , and deep learning-based techniques [14] . ...
arXiv:2202.11891v2
fatcat:svuq6tbmbjad5aykflpygefnra
SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
[article]
2020
arXiv
pre-print
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In addition, SenseCare is clinic-oriented and supports a wide range of clinical applications such as diagnosis and surgical planning for lung cancer, pelvic tumor, coronary artery disease, etc. ...
To facilitate clinical research with Artificial Intelligence (AI), SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection ...
Besides real-time data synchronization, SenseCare also supports pulling data directly from PACS/RIS based on user-defined rules. ...
arXiv:2004.07031v1
fatcat:aczelk3365ftniy2vt7tcxwe7i
Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems
2021
Frontiers in Digital Health
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physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. ...
An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. ...
On average, based on Table 7 , A-PBNRR with deep learning is ∼8.45 times better than rigid registration, ∼6.71 times better than B-Spline registration, and ∼7.9 times better than PBNRR. ...
doi:10.3389/fdgth.2020.613608
pmid:34713074
pmcid:PMC8521897
fatcat:5edxya6tovekncnq3wavu77y2q
i3PosNet: Instrument Pose Estimation from X-Ray in temporal bone surgery
[article]
2020
arXiv
pre-print
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Conclusion: The translation of Deep Learning based methods to surgical applications is difficult, because large representative datasets for training and testing are not available. ...
It outperforms conventional image registration-based approaches reducing average and maximum errors by at least two thirds. i3PosNet trained on synthetic images generalizes to real x-rays without any further ...
Previous non-Deep Learning pipelines based on 2D/3D registration [12] and template matching [26] achieve submillimeter accuracy for simple geometries. ...
arXiv:1802.09575v2
fatcat:gd6gzecy2rccbcsmvzjs6tpdci
The Future of Endoscopic Navigation: A Review of Advanced Endoscopic Vision Technology
2021
IEEE Access
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These techniques help surgeons or surgical robots locate instruments and lesions and expand the field of view of the endoscope. ...
Endoscopic vision is a specific application of computer vision involving the use of endoscopes that include instrument tracking, endoscopic view expansion, and suspicious lesion tracking in the application ...
Xiao Liang at Sir Run-Run Shaw Hospital, Hangzhou, China, for helping inspire the development of experiments and this article. ...
doi:10.1109/access.2021.3065104
fatcat:nprqk4gjhnhbrjvfbrqouoee6y
m2caiSeg: Semantic Segmentation of Laparoscopic Images using Convolutional Neural Networks
[article]
2020
arXiv
pre-print
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To address the identification of human anatomy and the surgical settings, we propose a deep learning based semantic segmentation algorithm to identify and label the tissues and organs in the endoscopic ...
We propose a new dataset and a deep learning method for pixel level identification of various organs and instruments in a endoscopic surgical scene. ...
In Section 4, we present the results of our network on the dataset. Finally, in Section 5, we conclude the paper and present some potential directions for future work. ...
arXiv:2008.10134v2
fatcat:dnqcythdpbghrk32x5yetntjfe
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
[article]
2018
arXiv
pre-print
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Based on the results of the validation study, we arrive at the conclusion that modern deep learning approaches outperform other methods in instrument segmentation tasks, but the results are still not perfect ...
Since additional hardware like tracking systems or the robot encoders are cumbersome and lack accuracy, surgical vision is evolving as promising techniques to segment and track the instruments using only ...
Here acquiring more annotated data might be the key to improve results of the machine learning based tracking methods, but acquiring large quantities of training data is challenging. ...
arXiv:1805.02475v1
fatcat:hagoe34yfzdd7drq5f5fmhkdqa
Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery
[article]
2020
arXiv
pre-print
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We propose a new deep learning based model for parallel processing of both laparoscopic and simulation images for robust segmentation of surgical tools. ...
Semantic tool segmentation in surgical videos is important for surgical scene understanding and computer-assisted interventions as well as for the development of robotic automation. ...
[19] : their method is based on a particle filter optimization that repeatedly updates the pose of the tool to match the silhouette projection of the surgical tool with a vision-based segmentation mask ...
arXiv:2007.09107v2
fatcat:nqlmtod7grayrjpntfurfu6shy
Learned optical flow for intra-operative tracking of the retinal fundus
2020
International Journal of Computer Assisted Radiology and Surgery
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The U-Net-based network trained on the synthetic dataset is shown to generalise well to the benchmark of real surgical videos. ...
We evaluate optical flow estimation by tracking a grid and sparsely annotated ground truth points on a benchmark of challenging real intra-operative clips obtained from an extensive internally acquired ...
The views expressed are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care. ...
doi:10.1007/s11548-020-02160-9
pmid:32323210
pmcid:PMC7261285
fatcat:ti5e32cukrddxeeh3jqfdwqbry
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