Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets.
OBJECTIVE
Quantitative analysis of cine cardiac magnetic resonance (CMR) images for the assessment of global left ventricular morphology and function remains a routine task in clinical cardiology practice. To date, this process requires user interaction and therefore prolongs the examination (i.e. cost) and introduces observer variability. In this study, we sought to validate the feasibility, accuracy, and time efficiency of a novel framework for automatic quantification of left ventricular global function in a clinical setting.


RESULTS
Analyses of 318 CMR studies, acquired at the enrolment of patients in a multi-centre imaging trial (DOPPLER-CIP), were performed automatically, as well as manually. For comparative purposes, intra- and inter-observer variability was also assessed in a subset of patients. The extracted morphological and functional parameters were compared between both analyses, and time efficiency was evaluated. The automatic analysis was feasible in 95% of the cases (302/318) and showed a good agreement with manually derived reference measurements, with small biases and narrow limits of agreement particularly for end-diastolic volume (-4.08 ± 8.98 mL), end-systolic volume (1.18 ± 9.74 mL), and ejection fraction (-1.53 ± 4.93%). These results were comparable with the agreement between two independent observers. A complete automatic analysis took 5.61 ± 1.22 s, which is nearly 150 times faster than manual contouring (14 ± 2 min, P < 0.05).


CONCLUSIONS
The proposed automatic framework provides a fast, robust, and accurate quantification of relevant left ventricular clinical indices in 'real-world' cine CMR images.
Lab on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães Portugal Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal sandroqueiros@ecsaude.uminho.pt.
Sandro
S
Queirós
ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães Portugal DIGARC-Polytechnic Institute of Cávado and Ave, Barcelos, Portugal.
Daniel
D
Barbosa
Department of Clinical Physiology, Linköping University, Linköping, Sweden Department of Medical and Health Sciences, Linköping University, Linköping, Sweden Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Jan
J
Engvall
Department of Medical and Health Sciences, Linköping University, Linköping, Sweden Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
Tino
T
Ebbers
Institute for Experimental and Translational Cardiovascular Imaging, DZHK Centre for Cardiovascular Imaging, University Hospital Frankfurt/Main, Germany.
Eike
E
Nagel
Department of Cardiology and Center for Cardiological Innovation, Oslo University Hospital, Rikshospitalet, Oslo, Norway.
Sebastian I
SI
Sarvari
Lab on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium.
Piet
P
Claus
Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal.
Jaime C
JC
Fonseca
ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães Portugal DIGARC-Polytechnic Institute of Cávado and Ave, Barcelos, Portugal.
João L
JL
Vilaça
Lab on Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium.
Jan
J
D'hooge
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001331:Automation; D015331:Cohort Studies; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008297:Male; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000000981:diagnostic imaging*; D015588:Observer Variation; D011237:Predictive Value of Tests; D011786:Quality Control; D013318:Stroke Volume / Q000502:physiology; D016277:Ventricular Function, Left / Q000502:physiology*
automatic segmentation; cardiac cine MRI; clinical validation; fast image processing; left ventricular quantification