TeCNO: Surgical Phase Recognition with Multi-stage Temporal Convolutional Networks
Czempiel T, Paschali M, Keicher M, Simson W, Feussner H, Kim ST, Navab N (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12263 LNCS
Pages Range: 343-352
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030597153
DOI: 10.1007/978-3-030-59716-0_33
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.
Involved external institutions
Germany (DE)How to cite
APA:
Czempiel, T., Paschali, M., Keicher, M., Simson, W., Feussner, H., Kim, S.T., & Navab, N. (2020). TeCNO: Surgical Phase Recognition with Multi-stage Temporal Convolutional Networks. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 343-352). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Czempiel, Tobias, et al. "TeCNO: Surgical Phase Recognition with Multi-stage Temporal Convolutional Networks." Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, Springer Science and Business Media Deutschland GmbH, 2020. 343-352.
BibTeX: Download