  Cardiac Magnetic Resonance (CMR) imaging is commonly used to assess cardiac
structure and function. One disadvantage of CMR is that post-processing of
exams is tedious. Without automation, precise assessment of cardiac function
via CMR typically requires an annotator to spend tens of minutes per case
manually contouring ventricular structures. Automatic contouring can lower the
required time per patient by generating contour suggestions that can be lightly
modified by the annotator. Fully convolutional networks (FCNs), a variant of
convolutional neural networks, have been used to rapidly advance the
state-of-the-art in automated segmentation, which makes FCNs a natural choice
for ventricular segmentation. However, FCNs are limited by their computational
cost, which increases the monetary cost and degrades the user experience of
production systems. To combat this shortcoming, we have developed the
FastVentricle architecture, an FCN architecture for ventricular segmentation
based on the recently developed ENet architecture. FastVentricle is 4x faster
and runs with 6x less memory than the previous state-of-the-art ventricular
segmentation architecture while still maintaining excellent clinical accuracy.
