  This study proposes a fully automated approach for the left atrial
segmentation from routine cine long-axis cardiac magnetic resonance image
sequences using deep convolutional neural networks and Bayesian filtering. The
proposed approach consists of a classification network that automatically
detects the type of long-axis sequence and three different convolutional neural
network models followed by unscented Kalman filtering (UKF) that delineates the
left atrium. Instead of training and predicting all long-axis sequence types
together, the proposed approach first identifies the image sequence type as to
2, 3 and 4 chamber views, and then performs prediction based on neural nets
trained for that particular sequence type. The datasets were acquired
retrospectively and ground truth manual segmentation was provided by an expert
radiologist. In addition to neural net based classification and segmentation,
another neural net is trained and utilized to select image sequences for
further processing using UKF to impose temporal consistency over cardiac cycle.
A cyclic dynamic model with time-varying angular frequency is introduced in UKF
to characterize the variations in cardiac motion during image scanning. The
proposed approach was trained and evaluated separately with varying amount of
training data with images acquired from 20, 40, 60 and 80 patients. Evaluations
over 1515 images with equal number of images from each chamber group acquired
from an additional 20 patients demonstrated that the proposed model
outperformed state-of-the-art and yielded a mean Dice coefficient value of
94.1%, 93.7% and 90.1% for 2, 3 and 4-chamber sequences, respectively, when
trained with datasets from 80 patients.
