Automated Myocardial Wall Motion Classification using Handcrafted Features vs a Deep CNN-based mapping.

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.