Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy.

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

https://www.ncbi.nlm.nih.gov/pubmed/33909264

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;: