  In echocardiography (echo), an electrocardiogram (ECG) is conventionally used
to temporally align different cardiac views for assessing critical
measurements. However, in emergencies or point-of-care situations, acquiring an
ECG is often not an option, hence motivating the need for alternative temporal
synchronization methods. Here, we propose Echo-SyncNet, a self-supervised
learning framework to synchronize various cross-sectional 2D echo series
without any external input. The proposed framework takes advantage of both
intra-view and inter-view self supervisions. The former relies on
spatiotemporal patterns found between the frames of a single echo cine and the
latter on the interdependencies between multiple cines. The combined
supervisions are used to learn a feature-rich embedding space where multiple
echo cines can be temporally synchronized. We evaluate the framework with
multiple experiments: 1) Using data from 998 patients, Echo-SyncNet shows
promising results for synchronizing Apical 2 chamber and Apical 4 chamber
cardiac views; 2) Using data from 3070 patients, our experiments reveal that
the learned representations of Echo-SyncNet outperform a supervised deep
learning method that is optimized for automatic detection of fine-grained
cardiac phase; 3) We show the usefulness of the learned representations in a
one-shot learning scenario of cardiac keyframe detection. Without any
fine-tuning, keyframes in 1188 validation patient studies are identified by
synchronizing them with only one labeled reference study. We do not make any
prior assumption about what specific cardiac views are used for training and
show that Echo-SyncNet can accurately generalize to views not present in its
training set. Project repository: github.com/fatemehtd/Echo-SyncNet.
