Instrument Tracking with Rigid Part Mixtures Model

2016 ◽  
pp. 22-34 ◽  
Author(s):  
Daniel Wesierski ◽  
Grzegorz Wojdyga ◽  
Anna Jezierska
Keyword(s):  
Instrument Tracking

Laboratory Investigation:Automated Instrument Tracking in Robotically Assisted Laparoscopic Surgery

1995 ◽  
Vol 1 (6) ◽  
pp. 308-325 ◽  
Author(s):  
Darrin R. Uecker ◽  
Y. F. Wang ◽  
Cheolwhan Lee ◽  
Yulun Wang
Keyword(s):  
Laparoscopic Surgery ◽  
Instrument Tracking

scholarly journals Intrasurgical Human Retinal Imaging With Manual Instrument Tracking Using a Microscope-Integrated Spectral-Domain Optical Coherence Tomography Device

2015 ◽  
Vol 4 (4) ◽  
pp. 1 ◽  
Author(s):  
Paul Hahn ◽  
Oscar Carrasco-Zevallos ◽  
David Cunefare ◽  
Justin Migacz ◽  
Sina Farsiu ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Retinal Imaging ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Instrument Tracking

scholarly journals EVA: Laparoscopic Instrument Tracking Based on Endoscopic Video Analysis for Psychomotor Skills Assessment

2012 ◽  
Vol 27 (3) ◽  
pp. 1029-1039 ◽  
Author(s):  
Ignacio Oropesa ◽  
Patricia Sánchez-González ◽  
Magdalena K. Chmarra ◽  
Pablo Lamata ◽  
Álvaro Fernández ◽  
...  
Keyword(s):  
Video Analysis ◽  
Skills Assessment ◽  
Psychomotor Skills ◽  
Laparoscopic Instrument ◽  
Endoscopic Video ◽  
Instrument Tracking

scholarly journals Real-Time Surgical Problem Detection and Instrument Tracking in Cataract Surgery

2020 ◽  
Vol 9 (12) ◽  
pp. 3896
Author(s):  
Shoji Morita ◽  
Hitoshi Tabuchi ◽  
Hiroki Masumoto ◽  
Hirotaka Tanabe ◽  
Naotake Kamiura
Keyword(s):  
Cataract Surgery ◽  
Real Time ◽  
False Positive Rate ◽  
Surgical Techniques ◽  
Surgical Skill ◽  
Skill Levels ◽  
Detection Rates ◽  
Surgical Methods ◽  
Instrument Tracking ◽  
Positive Rate

Surgical skill levels of young ophthalmologists tend to be instinctively judged by ophthalmologists in practice, and hence a stable evaluation is not always made for a single ophthalmologist. Although it has been said that standardizing skill levels presents difficulty as surgical methods vary greatly, approaches based on machine learning seem to be promising for this objective. In this study, we propose a method for displaying the information necessary to quantify the surgical techniques of cataract surgery in real-time. The proposed method consists of two steps. First, we use InceptionV3, an image classification network, to extract important surgical phases and to detect surgical problems. Next, one of the segmentation networks, scSE-FC-DenseNet, is used to detect the cornea and the tip of the surgical instrument and the incisional site in the continuous curvilinear capsulorrhexis, a particularly important phase in cataract surgery. The first and second steps are evaluated in terms of the area under curve (i.e., AUC) of the figure of the true positive rate versus (1—false positive rate) and the intersection over union (i.e., IoU) obtained by the ground truth and prediction associated with the region of interest. As a result, in the first step, the network was able to detect surgical problems with an AUC of 0.97. In the second step, the detection rate of the cornea was 99.7% when the IoU was 0.8 or more, and the detection rates of the tips of the forceps and the incisional site were 86.9% and 94.9% when the IoU was 0.1 or more, respectively. It was thus expected that the proposed method is one of the basic techniques to achieve the standardization of surgical skill levels.

Accessible Laparoscopic Instrument Tracking (“InsTrac”): Construct Validity in a Take-Home Box Simulator

2014 ◽  
Vol 24 (8) ◽  
pp. 578-583 ◽  
Author(s):  
Roland W. Partridge ◽  
Mark A. Hughes ◽  
Paul M. Brennan ◽  
Iain A.M. Hennessey
Keyword(s):  
Construct Validity ◽  
Laparoscopic Instrument ◽  
Instrument Tracking
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