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Gonzales RA, Seemann F, Lamy J, Arvidsson PM, Heiberg E, Murray V, Peters DC. Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours. BMC Med Imaging 2021; 21:101. [PMID: 34147081 PMCID: PMC8214286 DOI: 10.1186/s12880-021-00630-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 05/10/2021] [Indexed: 12/28/2022] Open
Abstract
Background Segmentation 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. Methods This 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. Results The 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. Conclusion The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function. Video Abstract
Supplementary Information The online version contains supplementary material available at 10.1186/s12880-021-00630-3.
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Affiliation(s)
- Ricardo A Gonzales
- 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
| | - Felicia Seemann
- 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
| | - Jérôme Lamy
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Per M Arvidsson
- Department of Clinical Physiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Einar Heiberg
- 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
| | - Victor Murray
- 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
| | - Dana C Peters
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America.
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Zhu L, Gao Y, Yezzi A, Tannenbaum A. Automatic segmentation of the left atrium from MR images via variational region growing with a moments-based shape prior. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:5111-22. [PMID: 24058026 PMCID: PMC4000445 DOI: 10.1109/tip.2013.2282049] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
The planning and evaluation of left atrial ablation procedures are commonly based on the segmentation of the left atrium, which is a challenging task due to large anatomical variations. In this paper, we propose an automatic approach for segmenting the left atrium from magnetic resonance imagery. The segmentation problem is formulated as a problem in variational region growing. In particular, the method starts locally by searching for a seed region of the left atrium from an MR slice. A global constraint is imposed by applying a shape prior to the left atrium represented by Zernike moments. The overall growing process is guided by the robust statistics of intensities from the seed region along with the shape prior to capture the entire atrial region. The robustness and accuracy of our approach are demonstrated by experimental results from 64 human MR images.
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Affiliation(s)
- Liangjia Zhu
- Department of Electrical and Computer Engineering, and the Comprehensive Cancer Center, University of Alabama, Birmingham, AL 35294 USA ()
| | - Yi Gao
- Department of Electrical and Computer Engineering, and the Comprehensive Cancer Center, University of Alabama, Birmingham, AL 35294 USA ()
| | - Anthony Yezzi
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30303 USA ()
| | - Allen Tannenbaum
- Department of Computer Science and Applied Mathematics/Statistics, Stony Brook University, Stony Brook, NY 11794 USA ()
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