Open AccessSegmentation of carotid arterial walls using neural networks
Author(s) -
Daniel D. Samber,
Sarayu Ramachandran,
A. Sahota,
Sonum Naidu,
Alison Pruzan,
Zahi A. Fayad,
V. R. S. Mani
Publication year - 2020
Publication title -
world journal of radiology
Language(s) - English
Resource type - Journals
ISSN - 1949-8470
DOI - 10.4329/wjr.v12.i1.1
Subject(s) - segmentation , convolutional neural network , sørensen–dice coefficient , artificial intelligence , lumen (anatomy) , pearson product moment correlation coefficient , medicine , magnetic resonance imaging , pattern recognition (psychology) , carotid arteries , computer science , correlation coefficient , image segmentation , radiology , machine learning , mathematics , statistics , surgery
Automated, accurate, objective, and quantitative medical image segmentation has remained a challenging goal in computer science since its inception. This study applies the technique of convolutional neural networks (CNNs) to the task of segmenting carotid arteries to aid in the assessment of pathology.