An automated skills assessment framework for laparoscopic training tasks

Various sensors and methods are used for evaluating trainees' skills in laparoscopic procedures. These methods are usually task‐specific and involve high costs or advanced setups.

[1]  Ken Masamune,et al.  Scrub nurse robot system-intraoperative motion analysis of a scrub nurse and timed-automata-based model for surgery , 2005, IEEE Transactions on Industrial Electronics.

[2]  Qiang Zhang,et al.  Video-based analysis of motion skills in simulation-based surgical training , 2013, Electronic Imaging.

[3]  Baoxin Li,et al.  Affordable, web-based surgical skill training and evaluation tool , 2016, J. Biomed. Informatics.

[4]  G. Hirzinger,et al.  Real-time visual servoing for laparoscopic surgery. Controlling robot motion with color image segmentation , 1997, IEEE Engineering in Medicine and Biology Magazine.

[5]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[6]  G. Fried,et al.  Proving the Value of Simulation in Laparoscopic Surgery , 2004, Annals of surgery.

[7]  Constantinos Loukas,et al.  An integrated approach to endoscopic instrument tracking for augmented reality applications in surgical simulation training , 2013, The international journal of medical robotics + computer assisted surgery : MRCAS.

[8]  Carla M Pugh,et al.  Working volume: validity evidence for a motion-based metric of surgical efficiency. , 2016, American journal of surgery.

[9]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[10]  Irfan Essa,et al.  Video Based Assessment of OSATS Using Sequential Motion Textures , 2014 .

[11]  S K Wirtschafter,et al.  Endoscopic appendectomy. , 1976, Gastrointestinal endoscopy.

[12]  Borko Furht,et al.  Motion estimation algorithms for video compression , 1996 .

[13]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[14]  V. Lahanas,et al.  A novel augmented reality simulator for skills assessment in minimal invasive surgery , 2015, Surgical Endoscopy.

[15]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[16]  J. Shuhaiber,et al.  Augmented reality in surgery. , 2004, Archives of surgery.

[17]  Jiebo Luo,et al.  A computer vision-based approach to grade simulated cataract surgeries , 2014, Machine Vision and Applications.

[18]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[19]  Baoxin Li,et al.  Video-based motion expertise analysis in simulation-based surgical training using hierarchical dirichlet process hidden markov model , 2011, MMAR '11.

[20]  Paolo Dario,et al.  Tracking endoscopic instruments without a localizer: A shape-analysis-based approach , 2007, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[21]  Luciano da Fontoura Costa,et al.  Shape Analysis and Classification: Theory and Practice , 2000 .

[22]  Henry C. Lin,et al.  Review of methods for objective surgical skill evaluation , 2011, Surgical Endoscopy.

[23]  Nassir Navab,et al.  Modeling and Segmentation of Surgical Workflow from Laparoscopic Video , 2010, MICCAI.

[24]  K Semm,et al.  Endoscopic Appendectomy , 1983, Endoscopy.

[25]  Rafael Grompone von Gioi,et al.  LSD: A Fast Line Segment Detector with a False Detection Control , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Irfan A. Essa,et al.  Augmenting Bag-of-Words: Data-Driven Discovery of Temporal and Structural Information for Activity Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Irfan A. Essa,et al.  Automated Assessment of Surgical Skills Using Frequency Analysis , 2015, MICCAI.

[28]  Martin D. Fox,et al.  Classifying mammographic lesions using computerized image analysis , 1993, IEEE Trans. Medical Imaging.

[29]  Kanav Kahol,et al.  High-fidelity, low-cost, automated method to assess laparoscopic skills objectively. , 2012, Journal of surgical education.

[30]  Bruce Gooch,et al.  Color2Gray: salience-preserving color removal , 2005, SIGGRAPH 2005.

[31]  Fady T. Charbel,et al.  Virtual reality training in neurosurgery: Review of current status and future applications , 2011, Surgical neurology international.

[32]  Constantinos Loukas,et al.  Performance comparison of various feature detector‐descriptors and temporal models for video‐based assessment of laparoscopic skills , 2016, The international journal of medical robotics + computer assisted surgery : MRCAS.

[33]  Roland W Partridge,et al.  Accessible laparoscopic instrument tracking ("InsTrac"): construct validity in a take-home box simulator. , 2014, Journal of laparoendoscopic & advanced surgical techniques. Part A.

[34]  Gregory D. Hager,et al.  Surgical gesture classification from video and kinematic data , 2013, Medical Image Anal..