Oksuz, Ilkay ; Mukhopadhyay, Anirban ; Dharmakumar, Rohan ; Tsaftaris, Sotirios A. (2017)
Unsupervised Myocardial Segmentation for Cardiac BOLD.
In: IEEE Transactions on Medical Imaging, 36 (11)
doi: 10.1109/TMI.2017.2726112
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal 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, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set containing cardiac phase resolved BOLD MR and standard CINE MR image sequences acquired in baseline and is chemic condition across ten 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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Oksuz, Ilkay ; Mukhopadhyay, Anirban ; Dharmakumar, Rohan ; Tsaftaris, Sotirios A. |
| Art des Eintrags: | Bibliographie |
| Titel: | Unsupervised Myocardial Segmentation for Cardiac BOLD |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Transactions on Medical Imaging |
| Jahrgang/Volume einer Zeitschrift: | 36 |
| (Heft-)Nummer: | 11 |
| DOI: | 10.1109/TMI.2017.2726112 |
| URL / URN: | https://doi.org/10.1109/TMI.2017.2726112 |
| Kurzbeschreibung (Abstract): | A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal 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, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set containing cardiac phase resolved BOLD MR and standard CINE MR image sequences acquired in baseline and is chemic condition across ten 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. |
| Freie Schlagworte: | Optical flow, Magnetic resonance imaging (MRI) |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 05 Mai 2020 15:01 |
| Letzte Änderung: | 05 Mai 2020 15:01 |
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