Point detection through multi-instance deep heatmap regression for sutures in endoscopy

• Verfasst von: Sharan, Lalith [VerfasserIn]
• Verfasst von: Romano, Gabriele [VerfasserIn]
• Verfasst von: Brand, Julian [VerfasserIn]
• Verfasst von: Kelm, Halvar [VerfasserIn]
• Verfasst von: Karck, Matthias [VerfasserIn]
• Verfasst von: De Simone, Raffaele [VerfasserIn]
• Verfasst von: Engelhardt, Sandy [VerfasserIn]
• Titel: Point detection through multi-instance deep heatmap regression for sutures in endoscopy
• Verf.angabe: Lalith Sharan, Gabriele Romano, Julian Brand, Halvar Kelm, Matthias Karck, Raffaele De Simone, Sandy Engelhardt
• E-Jahr: 2021
• Jahr: 08 November 2021
• Umfang: 11 S.
• Fussnoten: Gesehen am 15.09.2023
• Titel Quelle: Enthalten in: International journal of computer assisted radiology and surgery
• Ort Quelle: Berlin : Springer, 2006
• Jahr Quelle: 2021
• Band/Heft Quelle: 16(2021), 12, Seite 2107-2117
• ISSN Quelle: 1861-6429
• DOI: doi:10.1007/s11548-021-02523-w
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Endoscopy
• Sach-SW: Mitral valve repair
• Sach-SW: Point detection
• K10plus-PPN: 1859579728
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

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.