Paper Info
Title
Automated Myocardial Wall Motion Classification using Handcrafted Features vs a Deep CNN-based mapping.
Abstract
Compared to other modalities such as computed tomography or magnetic resonance imaging, the appearance of ultrasound images is highly dependent on the expertise of the sonographer or clinician making the image acquisition, as well as the machine used, making it a challenge to analyze due to the frequent presence of artefacts, missing boundaries, attenuation, shadows, and speckle. In addition, manual contouring of the epicardial and endocardial walls exhibits large inconsistencies and variations as it is strongly dependent on the sonographer's training and expertise. Hence, in this paper we propose a fully automated image analysis framework to ultimately perform wall motion abnormality classification in 2D+T images. We explore both traditional Random Forests classification with handcrafted features and spatio-temporal hierarchical aggregation of information with a deep learning CNN-based approach. Regarding the later classifier, we also investigate the effect of local phase information retrieval through the use of Feature Asymmetry (FA), and demonstrate that pre-processing videos with FA enables the spatio-temporal CNN to better discover relevant left ventricle endocardial abstractions from low-level features to high-level representations automatically.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8513063
EMBC
Field
DocType
Volume
Computer vision,Speckle pattern,Task analysis,Computer science,Feature extraction,Artificial intelligence,Deep learning,Classifier (linguistics),Contouring,Random forest,Sonographer
Conference
2018
Citations 
PageRank 
References 
1
0.37
0
Authors
5
Name
Order
Citations
PageRank
Hasmila A. Omar120.74
Arijit Patra221.75
João S. Domingos3112.73
Paul Leeson493.05
J. Alison Noble52001203.21