title: Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT.
abstract: 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.
  
  
  BACKGROUND
  NCT01379261. NCT01374321.
authors.affiliation: Department of Clinical Physiology, Skåne University Hospital in Lund, Lund University, Lund, Sweden. jane.tufvesson@med.lu.se.|Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden. jane.tufvesson@med.lu.se.
authors.forename: Jane
authors.initials: J
authors.lastname: Tufvesson
authors.affiliation: Department of Clinical Physiology, Skåne University Hospital in Lund, Lund University, Lund, Sweden. marcus.carlsson@med.lu.se.
authors.forename: Marcus
authors.initials: M
authors.lastname: Carlsson
authors.affiliation: Department of Clinical Physiology, Skåne University Hospital in Lund, Lund University, Lund, Sweden. aletras@hotmail.com.|Laboratory of Medical Informatics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece. aletras@hotmail.com.
authors.forename: Anthony H
authors.initials: AH
authors.lastname: Aletras
authors.affiliation: Department of Clinical Physiology, Skåne University Hospital in Lund, Lund University, Lund, Sweden. henrik.engblom@med.lu.se.
authors.forename: Henrik
authors.initials: H
authors.lastname: Engblom
authors.affiliation: Department of Cardiology, Henri Mondor Hospital, Creteil, France. jean-francois.deux@hmn.aphp.fr.
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Deux
authors.affiliation: Department of Cardiology, Lund University, Lund, Sweden. sasha.koul@med.lu.se.
authors.forename: Sasha
authors.initials: S
authors.lastname: Koul
authors.affiliation: Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. peder.sorensson@karolinska.se.
authors.forename: Peder
authors.initials: P
authors.lastname: Sörensson
authors.affiliation: Department of Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. john.pernow@ki.se.
authors.forename: John
authors.initials: J
authors.lastname: Pernow
authors.affiliation: Department of Cardiology B, Oslo, University Hospital Ullevål and Faculty of Medicine, University of Oslo, Oslo, Norway. dan.atar@online.no.
authors.forename: Dan
authors.initials: D
authors.lastname: Atar
authors.affiliation: Department of Cardiology, Lund University, Lund, Sweden. david.erlinge@med.lu.se.
authors.forename: David
authors.initials: D
authors.lastname: Erlinge
authors.affiliation: Department of Clinical Physiology, Skåne University Hospital in Lund, Lund University, Lund, Sweden. hakan.arheden@med.lu.se.
authors.forename: Håkan
authors.initials: H
authors.lastname: Arheden
authors.affiliation: Department of Clinical Physiology, Skåne University Hospital in Lund, Lund University, Lund, Sweden. einar.heiberg@med.lu.se.|Department of Biomedical Engineering, Faculty of Engineering, Lund University, Lund, Sweden. einar.heiberg@med.lu.se.
authors.forename: Einar
authors.initials: E
authors.lastname: Heiberg
mesh_terms: D000465:Algorithms; D006801:Humans; D018810:Magnetic Resonance Angiography / Q000379:methods*; D009203:Myocardial Infarction / Q000175:diagnosis*; D009206:Myocardium / Q000473:pathology*; D015588:Observer Variation; D054928:Validation Studies as Topic