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

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9126)


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.


  • Dictionary learning
  • Segmentation

The first two authors contributed equally to this work.

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  • DOI: 10.1007/978-3-319-20309-6_22
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This work was supported by the National Institutes of Health under Grant 2R01HL091989-05.

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Correspondence to Ilkay Oksuz .

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Mukhopadhyay, A., Oksuz, I., Bevilacqua, M., Dharmakumar, R., Tsaftaris, S.A. (2015). Data-Driven Feature Learning for Myocardial Segmentation of CP-BOLD MRI. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham.

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