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Projects > ELECTRONICS > 2018 > IEEE > MEDICAL IMAGE PROCESSING
A fully automated 2D+time myocardial segmentation framework is proposed for Cardiac Magnetic Resonance (CMR) Blood-Oxygen-Level-Dependent (BOLD) datasets. Ischemia detection with CINE BOLD CMR relies on spatiotemporal patterns in myocardial intensity but these patterns also trouble supervised segmentation methods, the de-facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace. Our method is based on variational pre-processing and spatial regularization using Markov Random Fields (MRF), to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a dataset containing cardiac phase-resolved BOLD (CP-BOLD) MR and standard CINE MR image sequences acquired in baseline and ischemic condition across 10 canine subjects. Our unsupervised approach outperforms even supervised state-of-the-art segmentation techniques by at least 10% when using Dice to measure accuracy on BOLD data and performs at-par for standard CINE MR. Furthermore, a novel segmental analysis method attuned for BOLD time-series is utilized to demonstrate the effectiveness of the proposed method in preserving key BOLD patterns.
Markov Random Field (MRF), multi-scale discriminative dictionary learning (MSDDL) procedure.
The main contributions of this paper are: An unsupervised myocardial segmentation algorithm that uses dictionaries to jointly represent appearance and motion, trained on subject-specific data. The ability to extract meaningful data representations even when the data we learn from may not have the most precise annotation. Use of a variational spatio-temporal smoothing of the BOLD signal in a cardiac image sequence. Extensive segmentation performance analysis with both local and global measures. 
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