Eye-gaze driven surgical workflow segmentation

Authors:
A. James;D. Vieira;B. Lo;A. Darzi;G.-Z. Yang
Affiliations:
Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom;Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

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.