Sahu, Manish ; Szengel, Angelika ; Mukhopadhyay, Anirban ; Zachow, Stefan (2020)
Surgical phase recognition by learning phase transitions.
In: Current Directions in Biomedical Engineering, 6 (1)
doi: 10.1515/cdbme-2020-0037
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
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 interphase 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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Sahu, Manish ; Szengel, Angelika ; Mukhopadhyay, Anirban ; Zachow, Stefan |
| Art des Eintrags: | Bibliographie |
| Titel: | Surgical phase recognition by learning phase transitions |
| Sprache: | Englisch |
| Publikationsjahr: | 17 September 2020 |
| Verlag: | De Gruyter |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Current Directions in Biomedical Engineering |
| Jahrgang/Volume einer Zeitschrift: | 6 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1515/cdbme-2020-0037 |
| Kurzbeschreibung (Abstract): | 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 interphase 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. |
| Zusätzliche Informationen: | Art.No.: 20200037 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Dez 2020 08:18 |
| Letzte Änderung: | 16 Dez 2020 08:18 |
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