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

ABSTRACT:

Purpose

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).

Methods

The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ([Formula: see text]) and Mask-RCNN ([Formula: see text]), which are fed with single still-frames I(t). Th

SUBMITTER: Lazo JF 

PROVIDER: S-EPMC8166718 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature