  Pixelwise segmentation of the left ventricular (LV) myocardium and the four
cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is
an essential preprocessing step for a wide range of analyses. Variability in
contrast, appearance, orientation, and placement of the heart between patients,
clinical views, scanners, and protocols makes fully automatic semantic
segmentation a notoriously difficult problem. Here, we present ${\Omega}$-Net
(Omega-Net): a novel convolutional neural network (CNN) architecture for
simultaneous localization, transformation into a canonical orientation, and
semantic segmentation. First, an initial segmentation is performed on the input
image, second, the features learned during this initial segmentation are used
to predict the parameters needed to transform the input image into a canonical
orientation, and third, a final segmentation is performed on the transformed
image. In this work, ${\Omega}$-Nets of varying depths were trained to detect
five foreground classes in any of three clinical views (short axis, SA,
four-chamber, 4C, two-chamber, 2C), without prior knowledge of the view being
segmented. The architecture was trained on a cohort of patients with
hypertrophic cardiomyopathy and healthy control subjects. Network performance
as measured by weighted foreground intersection-over-union (IoU) was
substantially improved in the best-performing ${\Omega}$- Net compared with
U-Net segmentation without localization or orientation. In addition,
{\Omega}-Net was retrained from scratch on the 2017 MICCAI ACDC dataset, and
achieves state-of-the-art results on the LV and RV bloodpools, and performed
slightly worse in segmentation of the LV myocardium. We conclude this
architecture represents a substantive advancement over prior approaches, with
implications for biomedical image segmentation more generally.
