Eye-gaze driven surgical workflow segmentation.
In today's climate of clinical governance there is growing pressure on surgeons to demonstrate their competence, improve standards and reduce surgical errors. This paper presents a study on developing a novel eye-gaze driven technique for surgical assessment and workflow recovery. The proposed technique investigates the use of a Parallel Layer Perceptor (PLP) to automate the recognition of a key surgical step in a porcine laparoscopic cholecystectomy model. The classifier is eye-gaze contingent but combined with image based visual feature detection for improved system performance. Experimental results show that by fusing image instrument likelihood measures, an overall classification accuracy of 75% is achieved.
Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom. a.james@imperial.ac.uk
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Darzi
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Yang
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D005133:Eye Movements / Q000502:physiology*; D005403:Fixation, Ocular / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*; D013552:Swine; D013647:Task Performance and Analysis; D014584:User-Computer Interface