Point detection through multi-instance deep heatmap regression for sutures in endoscopy
Mitral valve repair is a complex minimally invasive surgery of the heart valve. In this context, suture detection from endoscopic images is a highly relevant task that provides quantitative information to analyse suturing patterns, assess prosthetic configurations and produce augmented reality visua...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
08 November 2021
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| In: |
International journal of computer assisted radiology and surgery
Year: 2021, Volume: 16, Issue: 12, Pages: 2107-2117 |
| ISSN: | 1861-6429 |
| DOI: | 10.1007/s11548-021-02523-w |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s11548-021-02523-w
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| Author Notes: | Lalith Sharan, Gabriele Romano, Julian Brand, Halvar Kelm, Matthias Karck, Raffaele De Simone, Sandy Engelhardt |
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| 520 | |a Mitral valve repair is a complex minimally invasive surgery of the heart valve. In this context, suture detection from endoscopic images is a highly relevant task that provides quantitative information to analyse suturing patterns, assess prosthetic configurations and produce augmented reality visualisations. Facial or anatomical landmark detection tasks typically contain a fixed number of landmarks, and use regression or fixed heatmap-based approaches to localize the landmarks. However in endoscopy, there are a varying number of sutures in every image, and the sutures may occur at any location in the annulus, as they are not semantically unique. | ||
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