2 research outputs found
Deep Learning Analysis of Cardiac MRI in Legacy Datasets:Multi-Ethnic Study of Atherosclerosis
The shape and motion of the heart provide essential clues to understanding
the mechanisms of cardiovascular disease. With the advent of large-scale
cardiac imaging data, statistical atlases become a powerful tool to provide
automated and precise quantification of the status of patient-specific heart
geometry with respect to reference populations. The Multi-Ethnic Study of
Atherosclerosis (MESA), begun in 2000, was the first large cohort study to
incorporate cardiovascular MRI in over 5000 participants, and there is now a
wealth of follow-up data over 20 years. Building a machine learning based
automated analysis is necessary to extract the additional imaging information
necessary for expanding original manual analyses. However, machine learning
tools trained on MRI datasets with different pulse sequences fail on such
legacy datasets. Here, we describe an automated atlas construction pipeline
using deep learning methods applied to the legacy cardiac MRI data in MESA. For
detection of anatomical cardiac landmark points, a modified VGGNet
convolutional neural network architecture was used in conjunction with a
transfer learning sequence between two-chamber, four-chamber, and short-axis
MRI views. A U-Net architecture was used for detection of the endocardial and
epicardial boundaries in short axis images. Both network architectures resulted
in good segmentation and landmark detection accuracies compared with
inter-observer variations. Statistical relationships with common risk factors
were similar between atlases derived from automated vs manual annotations. The
automated atlas can be employed in future studies to examine the relationships
between cardiac morphology and future events