Surgical phase recognition by learning phase transitions

accepted for publication
  • Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1% on overall precision for phase detection tasks and 12.5% on meanAP for tool recognition tasks.

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Metadaten
Author:Manish Sahu, Angelika Szengel, Anirban Mukhopadhyay, Stefan Zachow
Document Type:Article
Parent Title (English):Current Directions in Biomedical Engineering (CDBME)
Publisher:De Gruyter
Year of first publication:2020
Note:
Proceedings of the 19th Annual Conference of the German Society for Computer- and Robot-Assisted Surgery (CURAC)
DOI:https://doi.org/https://doi.org/10.1515/cdbme-2020-0037