James A, Vieira D, Lo B, Darzi A, Yang G Z
Royal Society/Wolfson Medical Image Computing Laboratory & Department of Biosurgery and Surgical Technology, Imperial College London, London, United Kingdom.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):110-7. doi: 10.1007/978-3-540-75759-7_14.
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.
在当今临床治理的大环境下，外科医生面临着越来越大的压力，需要证明自己的能力、提高标准并减少手术失误。本文介绍了一项关于开发一种用于手术评估和工作流程恢复的新型眼动驱动技术的研究。所提出的技术研究了使用并行层感知器（PLP）来自动识别猪腹腔镜胆囊切除术模型中的关键手术步骤。该分类器以眼动为条件，但与基于图像的视觉特征检测相结合，以提高系统性能。实验结果表明，通过融合图像器械似然性度量，可实现75%的总体分类准确率。
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