Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours

BMC Medical Imaging

Per M. Arvidsson¹  Einar Heiberg²  Victor Murray³  Dana C. Peters⁴  Jérôme Lamy⁴  Felicia Seemann⁵  Ricardo A. Gonzales⁶ 

[1] Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden;Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden;Department of Biomedical Engineering, Lund University, Lund, Sweden;Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden;Department of Electrical Engineering, Universidad de Ingeniería y Tecnología, Lima, Peru;John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, United States of America;Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States of America;Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America;Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America;Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden;Department of Biomedical Engineering, Lund University, Lund, Sweden;Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America;Department of Electrical Engineering, Universidad de Ingeniería y Tecnología, Lima, Peru;Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden;

关键词: Active contours;    Cardiovascular imaging;    Magnetic resonance imaging;    Left atrium;    Segmentation;   

DOI  :  10.1186/s12880-021-00630-3

来源: Springer

【 摘 要 】

BackgroundSegmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent.MethodsThis study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra’s algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects.ResultsThe proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm.ConclusionThe proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function.BYudjB8VV3EfzLANNEj5aUVideo Abstract

【 授权许可】

CC BY