Evaluating Convolutional Neural Network and Hidden Markov Model for Recognising Surgical Phases in Sigmoid Resection

Abstract

Surgical workflow analysis in laparoscopic surgeries has been studied widely during last years because of its various applications. For example, optimising the schedule of operating rooms (OR) and developing a context-aware system that supports surgical team during the intervention. Surgical phase recognition has been applied to various kinds of laparoscopic procedures, mainly of type cholecystectomy. Sigmoid resection procedures are considered more complex than cholecystectomy, and they have not been extensively studied. Therefore, the focus of this work is to study phase recognition in sigmoid resection. In this paper, a convolutional neural network (CNN) architecture and Hidden Markov Model (HMM) were evaluated for performing phase recognition in sigmoid resection videos. The CNN is an extension of a pretrained model, and it was fine-tuned to perform the recognition. To consider the temporal aspect of the phase sequences, confidences obtained by the CNN were then provided into a HMM to release final classification. Experimental results show a low performance of the proposed method to recognise surgical phases in such complex procedures. Therefore, the dataset used for the evaluation was also reviewed, and statistics of each phase were generated.

Keywords

• phase recognition
• sigmoid resection
• convolutional neural network (cnn)
• hidden markov model (hmm)