Sahu, Manish ; Strömsdörfer, Ronja ; Mukhopadhyay, Anirban ; Zachow, Stefan (2020)
Endo-Sim2Real: Consistency Learning-Based Domain Adaptation for Instrument Segmentation.
23rd International Conference on Medical Image Computing and Computer Assisted Intervention. virtual Conference (04.-08.10.)
doi: 10.1007/978-3-030-59716-0_75
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis’15 dataset) highlight the effectiveness of the proposed Endo-Sim2Real method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Sahu, Manish ; Strömsdörfer, Ronja ; Mukhopadhyay, Anirban ; Zachow, Stefan |
| Art des Eintrags: | Bibliographie |
| Titel: | Endo-Sim2Real: Consistency Learning-Based Domain Adaptation for Instrument Segmentation |
| Sprache: | Englisch |
| Publikationsjahr: | 29 September 2020 |
| Verlag: | Springer |
| Buchtitel: | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 |
| Veranstaltungstitel: | 23rd International Conference on Medical Image Computing and Computer Assisted Intervention |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 04.-08.10. |
| DOI: | 10.1007/978-3-030-59716-0_75 |
| Kurzbeschreibung (Abstract): | Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis’15 dataset) highlight the effectiveness of the proposed Endo-Sim2Real method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity. |
| Zusätzliche Informationen: | Part of the Lecture Notes in Computer Science book series (LNCS, volume 12263), Proceedings Part III |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 16 Dez 2020 08:14 |
| Letzte Änderung: | 16 Dez 2020 08:15 |
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