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.
| 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 |
| First Page: | 20200037 |
| Publisher: | De Gruyter |
| Publishing Institution: | Zuse Institute Berlin (ZIB) |
| Date of first Publication: | 2020/09/17 |
| Notes: | Nomination for the Best-Paper Award |
| DOI: | https://doi.org/https://doi.org/10.1515/cdbme-2020-0037 |
