Data-Driven Feature Learning for Myocardial Segmentation of CP-BOLD MRI

  • Cardiac Phase-resolved Blood Oxygen-Level-Dependent (CP- BOLD) MR is capable of diagnosing an ongoing ischemia by detecting changes in myocardial intensity patterns at rest without any contrast and stress agents. Visualizing and detecting these changes require significant post-processing, including myocardial segmentation for isolating the myocardium. But, changes in myocardial intensity pattern and myocardial shape due to the heart’s motion challenge automated standard CINE MR myocardial segmentation techniques resulting in a significant drop of segmentation accuracy. We hypothesize that the main reason behind this phenomenon is the lack of discernible features. In this paper, a multi scale discriminative dictionary learning approach is proposed for supervised learning and sparse representation of the myocardium, to improve the myocardial feature selection. The technique is validated on a challenging dataset of CP-BOLD MR and standard CINE MR acquired in baseline and ischemic condition across 10 canine subjects. The proposed method significantly outperforms standard cardiac segmentation techniques, including segmentation via registration, level sets and supervised methods for myocardial segmentation.

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Author:Anirban Mukhopadhyay, Ilkay Oksuz, Marco Bevilacqua, Rohan Dharmakumar, Sotirios Tsaftaris
Document Type:Article
Parent Title (English):Functional Imaging and Modeling of the Heart
Volume:9126
First Page:189
Last Page:197
Series:Lecture Notes in Computer Science
Publisher:Springer
Year of first publication:2015
DOI:http://dx.doi.org/10.1007/978-3-319-20309-6_22

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