  Two-dimensional echocardiography (2DE) measurements of left ventricle (LV)
dimensions are highly significant markers of several cardiovascular diseases.
These measurements are often used in clinical care despite suffering from large
variability between observers. This variability is due to the challenging
nature of accurately finding the correct temporal and spatial location of
measurement endpoints in ultrasound images. These images often contain fuzzy
boundaries and varying reflection patterns between frames. In this work, we
present a convolutional neural network (CNN) based approach to automate 2DE LV
measurements. Treating the problem as a landmark detection problem, we propose
a modified U-Net CNN architecture to generate heatmaps of likely coordinate
locations. To improve the network performance we use anatomically meaningful
heatmaps as labels and train with a multi-component loss function. Our network
achieves 13.4%, 6%, and 10.8% mean percent error on intraventricular septum
(IVS), LV internal dimension (LVID), and LV posterior wall (LVPW) measurements
respectively. The design outperforms other networks and matches or approaches
intra-analyser expert error.
