Data-driven feature learning for myocardial registration and segmentation

This chapter discusses novel techniques for the tasks of segmentation and registration separately and jointly. In particular, the feature learning is tested on cardiac phase-resolved blood oxygen-level-dependent (CP-BOLD) MR images, which is a new contrast agent- and stress-free imaging technique for the assessment of myocardial ischemia at rest. CP-BOLD MRI introduces varying contrast in medical image analysis applications. Therefore, establishing voxel to voxel correspondences throughout the cardiac sequence, an inevitable component of statistical analysis of these images remains challenging. Furthermore, medical background and specific segmentation difficulties associated to these images are present. Alongside with the inconsistency in myocardial intensity patterns, the changes in myocardial shape due to the heart’s motion lead to low registration performance for state-of-the-art methods.