Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT

作者：Tufvesson Jane; Carlsson Marcus; Aletras Anthony H; Engblom Henrik; Deux Jean Francois; Koul Sasha; Sorensson Peder; Pernow John; Atar Dan; Erlinge David; Arheden Hakan; Heiberg Einar^*

来源：BMC Medical Imaging, 2016, 16(1): 19.

DOI：10.1186/s12880-016-0124-1

• Quantification
• Myocardial salvage
• Randomized clinical trials
• Expectation maximization
• A priori information
• Surface coil correction

Background: 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.
Methods: 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).
Results: 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.
Conclusions: 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.

• 出版日期2016-3-5