Sahu, Manish ; Mukhopadhyay, Anirban ; Zachow, Stefan (2021)
Simulation-to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation.
In: International Journal of Computer Assisted Radiology and Surgery, 16 (5)
doi: 10.1007/s11548-021-02383-4
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Segmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts.
We introduce a teacher–student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation.
Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach.
We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Sahu, Manish ; Mukhopadhyay, Anirban ; Zachow, Stefan |
| Art des Eintrags: | Bibliographie |
| Titel: | Simulation-to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation |
| Sprache: | Englisch |
| Publikationsjahr: | 2021 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Computer Assisted Radiology and Surgery |
| Jahrgang/Volume einer Zeitschrift: | 16 |
| (Heft-)Nummer: | 5 |
| DOI: | 10.1007/s11548-021-02383-4 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Segmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. We introduce a teacher–student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation. Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach. We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting. |
| Freie Schlagworte: | Surgical instrument segmentation, Simulation-based learning, Self-supervision, Consistency learning, Self-ensembling, Unsupervised domain adaptation |
| Zusätzliche Informationen: | Erstveröffentlichung |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 30 Sep 2024 11:24 |
| Letzte Änderung: | 30 Sep 2024 11:24 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Simulation-to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation. (deposited 24 Sep 2024 09:11)
- Simulation-to-real domain adaptation with teacher–student learning for endoscopic instrument segmentation. (deposited 30 Sep 2024 11:24) [Gegenwärtig angezeigt]
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