TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks

• URL: http://arxiv.org/abs/2003.10751v1
• Date: Tue, 24 Mar 2020 10:12:30 GMT
• Title: TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
• Authors: Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab
• Abstract summary: We propose a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information.
• Score: 43.95869213955351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Causal, dilated convolutions allow for a large receptive field and online inference with smooth predictions even during ambiguous transitions. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information. Outperforming various state-of-the-art LSTM approaches, we verify the suitability of the proposed causal MS-TCN for surgical phase recognition.

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