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Unsupervised Myocardial Segmentation for Cardiac MRI

Mukhopadhyay, Anirban and Oksuz, Ilkay and Bevilacqua, Marco and Dharmakumar, Rohan and Tsaftaris, Sotirios A. Unsupervised Myocardial Segmentation for Cardiac MRI. In: Medical Image Computing and Computer-Assisted Intervention. MICCAI 2015. Lecture Notes in Computer Science, 9351 . Springer, pp. 12-20. ISBN 978-3-319-24573-7 (2015)

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Though unsupervised segmentation was a de-facto standard for cardiac MRI segmentation early on, recently cardiac MRI segmentation literature has favored fully supervised techniques such as Dictionary Learning and Atlas-based techniques. But, the benefits of unsupervised techniques e.g., no need for large amount of training data and better potential of handling variability in anatomy and image contrast, is more evident with emerging cardiac MR modalities. For example, CP-BOLD is a new MRI technique that has been shown to detect ischemia without any contrast at stress but also at rest conditions. Although CP-BOLD looks similar to standard CINE, changes in myocardial intensity patterns and shape across cardiac phases, due to the heart’s motion, BOLD effect and artifacts affect the underlying mechanisms of fully supervised segmentation techniques resulting in a significant drop in segmentation accuracy. In this paper, we present a fully unsupervised technique for segmenting myocardium from the background in both standard CINE MR and CP-BOLD MR. We combine appearance with motion information (obtained via Optical Flow) in a dictionary learning framework to sparsely represent important features in a low dimensional space and separate myocardium from background accordingly. Our fully automated method learns background-only models and one class classifier provides myocardial segmentation. The advantages of the proposed technique are demonstrated on a dataset containing CP-BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across 10 canine subjects, where our method outperforms state-of-the-art supervised segmentation techniques in CP-BOLD MR and performs at-par for standard CINE MR.

Item Type: Book Section
Identification Number: 10.1007/978-3-319-24574-4_2
Uncontrolled Keywords: Unsupervised Segmentation; Dictionary Learning; BOLD; CINE; MRI
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Q Science > QM Human anatomy
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 13 Oct 2015 08:07
Last Modified: 13 Oct 2015 08:08
URI: http://eprints.imtlucca.it/id/eprint/2771

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