A semiautomatic method for rapid segmentation of velocity‐encoded myocardial magnetic resonance imaging data

• Published in: Magnetic resonance in medicine Vol. 78; no. 3; pp. 1199 - 1207
• Main Authors: Espe, Emil K. S., Skårdal, Kristine, Aronsen, Jan Magnus, Zhang, Lili, Sjaastad, Ivar
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.09.2017
• Subjects: Animals; Coding; Diastole; Heart - diagnostic imaging; Heart diseases; Image processing; Image Processing, Computer-Assisted - methods; Image segmentation; Integration; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Mapping; Masks; MRI; Myocardium; Phase contrast; postprocessing; Rats; Rats, Wistar; Resonance; segmentation; Systole; tissue phase mapping; Variability; Velocity; segmentation; magnetic resonance imaging; postprocessing; phase contrast; MRI; tissue phase mapping
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.26486

Purpose To develop a semiautomatic method for rapid segmentation of myocardial tissue phase mapping (TPM) data. Methods Manual segmentation of the myocardium was performed at end‐diastole and end‐systole. The points in both user‐defined masks were then automatically tracked over the entire cardiac cycle using temporal integration of the velocity field. Paths that failed to visit both masks at the expected times were excluded, after which masks for all time points were generated automatically from the accepted paths. Midventricular and basal phase contrast TPM slices from 12 rats were segmented using the proposed method and fully manual segmentation. The results were compared using Dice's metric and Bland–Altman analysis, and interobserver variability was assessed. Results The semiautomatic method reduced the average user input time from 21 min to 1 min per slice. The Dice metrics between the methods were 0.88 ± 0.03 (midventricular) and 0.83 ± 0.06 (basal), and Bland–Altman limits of agreement of peak systolic and diastolic regional velocities were: midventricular: 0.05 ± 0.65 cm/s, −0.02 ± 0.42 cm/s, and −0.03 ± 0.40 cm/s (radial, tangential, longitudinal); basal: −0.04 ± 0.73 cm/s, 0.03 ± 0.60 cm/s, and −0.04 ± 0.48 cm/s (radial, tangential, longitudinal). Interobserver variability following semiautomatic segmentation was lower than for manual segmentation. Conclusion The proposed method reduced the segmentation time substantially and exhibited well‐preserved data quality and excellent interobserver limits of agreement. Magn Reson Med 78:1199–1207, 2017. © 2016 International Society for Magnetic Resonance in Medicine.