Surgical phase recognition by learning phase transitions

  • 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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:Manish Sahu, Angelika Szengel, Anirban Mukhopadhyay, Stefan ZachowORCiD
Document Type:Article
Parent Title (English):Current Directions in Biomedical Engineering (CDBME)
Volume:6
Issue:1
Publisher:De Gruyter
Publishing Institution:Zuse Institute Berlin (ZIB)
Date of first Publication:2020/09/17
Note:
Nomination for the Best-Paper Award
DOI:https://doi.org/https://doi.org/10.1515/cdbme-2020-0037