Spatio-Temporal Features Based Surgical Phase Classification Using CNNs.

Abstract

In this paper, we propose a novel encoder-decoder based surgical phase classification technique leveraging on the spatio-temporal features extracted from the videos of laparoscopic cholecystectomy surgery. We use combined margin loss function to train on the computationally efficient PeleeNet architecture to extract features that exhibit: (1) Intra-phase similarity, (2) Inter-phase dissimilarity. Using these features, we propose to encapsulate sequential feature embeddings, 64 at a time and classify the surgical phase based on customized efficient residual factorized CNN architecture (ST-ERFNet). We obtained surgical phase classification accuracy of 86.07% on the publicly available Cholec80 dataset which consists of 7 surgical phases. The number of parameters required for the computation is approximately reduced by 84% and yet achieves comparable performance as the state of the art.Clinical relevance- Autonomous surgical phase classification sets the platform for automatically analyzing the entire surgical work flow. Additionally, could streamline the process of assessment of a surgery in terms of efficiency, early detection of errors or deviation from usual practice. This would potentially result in increased patient care.