Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy.
Fiche publication
Date publication
avril 2021
Journal
International journal of computer assisted radiology and surgery
Auteurs
Membres identifiés du Cancéropôle Est :
Pr DE MATHELIN Michel
Tous les auteurs :
Lazo JF, Marzullo A, Moccia S, Catellani M, Rosa B, de Mathelin M, De Momi E
Lien Pubmed
Résumé
Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs).
Mots clés
Convolutional neural networks, Deep learning, Image segmentation, Upper tract urothelial carcinoma (UTUC), Ureteroscopy
Référence
Int J Comput Assist Radiol Surg. 2021 Apr 28;: