Deep learning-enabled fluorescence imaging for surgical guidance: in silico training for oral cancer depth quantification

Oral cancer surgery requires accurate margin delineation to balance complete resection with post-operative functionality. Current fluorescence imaging systems provide two-dimensional margin assessment yet fail to quantify tumor depth prior to resection. Harnessing structured light in combination wit...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Journal of biomedical optics 2025-01, Vol.30 (Suppl 1), p.S13706
Hauptverfasser:	Won, Natalie J, Bartling, Mandolin, La Macchia, Josephine, Markevich, Stefanie, Holtshousen, Scott, Jagota, Arjun, Negus, Christina, Najjar, Esmat, Wilson, Brian C, Irish, Jonathan C, Daly, Michael J
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Simulation Deep Learning Humans Image Processing, Computer-Assisted - methods Margins of Excision Mouth Neoplasms - diagnostic imaging Mouth Neoplasms - pathology Mouth Neoplasms - surgery Neural Networks, Computer Optical Imaging - methods Phantoms, Imaging Special Issue on Molecular Guided Surgery Surgery, Computer-Assisted - methods
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!