A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images

Abstract

Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the complex spatial relationship between the heart and its neighboring structures, dynamic changes in heart shape, and limited image contrast, resolution, and volume coverage. In this study, we developed and evaluated a deep generative shape model-driven level set method to address these challenges. The proposed heart motion tracking method makes use of a heart shape model that characterizes the statistical variations in heart shapes present in a training data set. This heart shape model was established by training a three-layered deep Boltzmann machine (DBM) in order to characterize both local and global heart shape variations. During the tracking phase, a distance regularized level-set evolution (DRLSE) method was applied to delineate the heart contour on each frame of a cine MRI image sequence. The trained shape model was embedded into the DRLSE method as a shape prior term to constrain an evolutional shape to reach the desired heart boundary. Frame-by-frame heart motion tracking was achieved by iteratively mapping the obtained heart contour for each frame to the next frame as a reliable initialization, and performing a level-set evolution. The performance of the proposed motion tracking method was demonstrated using thirty-eight coronal cine MRI image sequences.

Machine intelligence in non-invasive endocrine cancer diagnostics

“…In this way, the unsupervised learning model can potentially elucidate trends that the investigator had not originally set out to find, arguably the greatest strength and weakness of this technique. Unsupervised techniques can also be leveraged for augmenting imaging workflows in the annotation and pre-processing of unlabelled data 27 , 28 . Again, a critical conceptual distinction between supervised and unsupervised learning is that the output for the former will typically be a defined label or value, whereas the latter will be a cluster or association.…”

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

“…The weights in the higher level of the architecture show spatial patterns that can identify specific tasks and the third layer represents distinct patterns or codes. In [118], a deep generative shape model–driven level set method was developed and evaluated to address automatic heart motion tracking to minimize radiation-induced cardiotoxicity. The proposed heart motion tracking method made use of MRI image sequences that characterize the statistical variations in heart shapes.…”

Deformable cardiac surface tracking by adaptive estimation algorithms

“…Much progress has been made in recent years, where deep learning and correlation filter based approaches have gained increasing attention 8 – 11 . Lately, there has been a growing interest in applying machine and deep learning based methods to cardiac motion segmentation and tracking problems 12 – 15 . The clinical applications of these techniques has been primarily focused on the diagnostic cases to assess cardiac function such as by providing accurate estimation of the right and left ventricular volumes, ejection ratios, and other quantitative indices, which is not the focus of this study.…”