Publikation: Action Recognition in Video Recordings ...
Stammdaten
Titel: | Action Recognition in Video Recordings from Gynecologic Laparoscopy |
Untertitel: | |
Kurzfassung: | Action recognition is a prerequisite for many applications in laparoscopic video analysis including but not limited to surgical training, operation room planning, follow-up surgery preparation, post-operative surgical assessment, and surgical outcome estimation. However, automatic action recognition in laparoscopic surgeries involves numerous challenges such as (I) cross-action and intra-action duration variation, (II) relevant content distortion due to smoke, blood accumulation, fast camera motions, organ movements, object occlusion, and (III) surgical scene variations due to different illuminations and viewpoints. Besides, action annotations in laparoscopy surgeries are limited and expensive due to requiring expert knowledge. In this study, we design and evaluate a CNN-RNN architecture as well as a customized training-inference framework to deal with the mentioned challenges in laparoscopic surgery action recognition. Using stacked recurrent layers, our proposed network takes advantage of inter-frame dependencies to negate the negative effect of content distortion and variation in action recognition. Furthermore, our proposed frame sampling strategy effectively manages the duration variations in surgical actions to enable action recognition with high temporal resolution. Our extensive experiments confirm the superiority of our proposed method in action recognition compared to static CNNs. |
Schlagworte: | laparoscopic surgery, action recognition, Convolutional neural networks, recurrent neural networks |
Publikationstyp: | Beitrag in Proceedings (Autorenschaft) |
Erscheinungsdatum: | 06.2023 (Print) |
Erschienen in: |
CBMS '23 Proceedings of the IEEE 36th International Symposium on Computer-Based Medical Systems
CBMS '23 Proceedings of the IEEE 36th International Symposium on Computer-Based Medical Systems
(
IEEE Xplore Digital Library;
)
zur Publikation |
Titel der Serie: | - |
Bandnummer: | - |
Erstveröffentlichung: | Ja |
Version: | - |
Seite: | S. 29 - 34 |
Versionen
Keine Version vorhanden |
Erscheinungsdatum: | 06.2023 |
ISBN: |
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ISSN: | 2372-9198 |
Homepage: | https://ieeexplore.ieee.org/document/10178763 |
Erscheinungsdatum: | 17.07.2023 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/cbms58004.2023.00187 |
Homepage: | https://ieeexplore.ieee.org/document/10178763 |
Open Access |
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AutorInnen
Sahar Nasirihaghighi (intern) | ||||
Negin Ghamsarian (extern) | ||||
Daniela Stefanics (intern) | ||||
Klaus Schöffmann (intern) | ||||
Heinrich Husslein
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Zuordnung
Organisation | Adresse | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
Kategorisierung
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Forschungscluster | Kein Forschungscluster ausgewählt |
Peer Reviewed |
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Publikationsfokus |
Klassifikationsraster der zugeordneten Organisationseinheiten:
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Arbeitsgruppen |
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Kooperationen
Organisation | Adresse | ||
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Unversity of Bern / Center for AI in Medicine
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CH - 3012 Bern |
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Medical University Vienna / Department of Gynecology and Gynecological Oncology
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AT - 1090 Wien |
Forschungsaktivitäten
(Achtung: Externe Aktivitäten werden im Suchergebnis nicht mitangezeigt)
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Publikationen: | Keine verknüpften Publikationen vorhanden |
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