Reliable segmentation of 2D cardiac magnetic resonance perfusion image sequences using time as the 3rd dimension.
Objectives: Cardiac magnetic resonance (CMR) first-pass perfusion is an established noninvasive diagnostic imaging modality for detecting myocardial ischemia. A CMR perfusion sequence provides a time series of 2D images for dynamic contrast enhancement of the heart. Accurate myocardial segmentation...
| Publicado en: | European Radiology Vol. 31; no. 6; pp. 3941 - 3951 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150343610&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150343610 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2021 vid: 31 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150343610 147258557 150343610 NLM33247342 150343610 10.1007/s00330-020-07474-5 NLM33247342 150343610 ppf: 3941 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Reliable segmentation of 2D cardiac magnetic resonance perfusion image sequences using time as the 3rd dimension. aug: au: Sandfort, Veit Jacobs, Matthew Arai, Andrew E. Hsu, Li-Yueh affil: Stanford Medicine, Pasteur Drive 300, 94305, Stanford, CA, USA sug: subj: Magnetic Resonance Imaging Heart Perfusion Magnetic Resonance Spectroscopy Psychological Tests Funding Source Human ab: Objectives: Cardiac magnetic resonance (CMR) first-pass perfusion is an established noninvasive diagnostic imaging modality for detecting myocardial ischemia. A CMR perfusion sequence provides a time series of 2D images for dynamic contrast enhancement of the heart. Accurate myocardial segmentation of the perfusion images is essential for quantitative analysis and it can facilitate automated pixel-wise myocardial perfusion quantification.Methods: In this study, we compared different deep learning methodologies for CMR perfusion image segmentation. We evaluated the performance of several image segmentation methods using convolutional neural networks, such as the U-Net in 2D and 3D (2D plus time) implementations, with and without additional motion correction image processing step. We also present a modified U-Net architecture with a novel type of temporal pooling layer which results in improved performance.Results: The best DICE scores were 0.86 and 0.90 for LV myocardium and LV cavity, while the best Hausdorff distances were 2.3 and 2.1 pixels for LV myocardium and LV cavity using 5-fold cross-validation. The methods were corroborated in a second independent test set of 20 patients with similar performance (best DICE scores 0.84 for LV myocardium).Conclusions: Our results showed that the LV myocardial segmentation of CMR perfusion images is best performed using a combination of motion correction and 3D convolutional networks which significantly outperformed all tested 2D approaches. Reliable frame-by-frame segmentation will facilitate new and improved quantification methods for CMR perfusion imaging.Key Points: • Reliable segmentation of the myocardium offers the potential to perform pixel level perfusion assessment. • A deep learning approach in combination with motion correction, 3D (2D + time) methods, and a deep temporal connection module produced reliable segmentation results. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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