Automated segmentation of subarachnoid hemorrhages with convolutional neural networks

Automated segmentation of subarachnoid hemorrhages with convolutional neural networks

Purpose: To investigate the viability of convolutional neural networks (CNNs) for the detection and volumetric segmentation of subarachnoid hemorrhage (SAH) in non-contrast computed tomography (NCCT). Materials and methods: We developed and trained a CNN for the SAH segmentation by splitting a set o...

Full description

Bibliographic Details
Main Authors: Renan Sales Barros, Wessel E. van der Steen, Anna M.M. Boers, IJsbrand Zijlstra, Rene van den Berg, Wassim El Youssoufi, Alexandre Urwald, Dagmar Verbaan, Peter Vandertop, Charles Majoie, Silvia Delgado Olabarriaga, Henk A. Marquering
Format: Article
Language:English
Published: Elsevier 2020-01-01
Series:Informatics in Medicine Unlocked
Subjects:
Subarachnoid hemorrhage
Hemorrhage segmentation
Hemorrhage detection
Convolutional neural network
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2352914820300769

Internet

http://www.sciencedirect.com/science/article/pii/S2352914820300769

Similar Items