Title |
Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT
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Published in |
BMC Medical Imaging, March 2016
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DOI | 10.1186/s12880-016-0124-1 |
Pubmed ID | |
Authors |
Jane Tufvesson, Marcus Carlsson, Anthony H. Aletras, Henrik Engblom, Jean-François Deux, Sasha Koul, Peder Sörensson, John Pernow, Dan Atar, David Erlinge, Håkan Arheden, Einar Heiberg |
Abstract |
Efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI = 1-MI/MaR). Cardiovascular magnetic resonance (CMR) can be used to assess MI by late gadolinium enhancement (LGE) and MaR by either T2-weighted imaging or contrast enhanced SSFP (CE-SSFP). Automatic segmentation algorithms have been developed and validated for MI by LGE as well as for MaR by T2-weighted imaging. There are, however, no algorithms available for CE-SSFP. Therefore, the aim of this study was to develop and validate automatic segmentation of MaR in CE-SSFP. The automatic algorithm applies surface coil intensity correction and classifies myocardial intensities by Expectation Maximization to define a MaR region based on a priori regional criteria, and infarct region from LGE. Automatic segmentation was validated against manual delineation by expert readers in 183 patients with reperfused acute MI from two multi-center randomized clinical trials (RCT) (CHILL-MI and MITOCARE) and against myocardial perfusion SPECT in an additional set (n = 16). Endocardial and epicardial borders were manually delineated at end-diastole and end-systole. Manual delineation of MaR was used as reference and inter-observer variability was assessed for both manual delineation and automatic segmentation of MaR in a subset of patients (n = 15). MaR was expressed as percent of left ventricular mass (%LVM) and analyzed by bias (mean ± standard deviation). Regional agreement was analyzed by Dice Similarity Coefficient (DSC) (mean ± standard deviation). MaR assessed by manual and automatic segmentation were 36 ± 10 % and 37 ± 11 %LVM respectively with bias 1 ± 6 %LVM and regional agreement DSC 0.85 ± 0.08 (n = 183). MaR assessed by SPECT and CE-SSFP automatic segmentation were 27 ± 10 %LVM and 29 ± 7 %LVM respectively with bias 2 ± 7 %LVM. Inter-observer variability was 0 ± 3 %LVM for manual delineation and -1 ± 2 %LVM for automatic segmentation. Automatic segmentation of MaR in CE-SSFP was validated against manual delineation in multi-center, multi-vendor studies with low bias and high regional agreement. Bias and variability was similar to inter-observer variability of manual delineation and inter-observer variability was decreased by automatic segmentation. Thus, the proposed automatic segmentation can be used to reduce subjectivity in quantification of MaR in RCT. NCT01379261 . NCT01374321 . |
X Demographics
Geographical breakdown
Country | Count | As % |
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Australia | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 2% |
Unknown | 49 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 7 | 14% |
Researcher | 7 | 14% |
Student > Bachelor | 6 | 12% |
Other | 4 | 8% |
Student > Master | 4 | 8% |
Other | 6 | 12% |
Unknown | 16 | 32% |
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Medicine and Dentistry | 18 | 36% |
Computer Science | 5 | 10% |
Engineering | 4 | 8% |
Psychology | 2 | 4% |
Mathematics | 1 | 2% |
Other | 3 | 6% |
Unknown | 17 | 34% |