Name
Abstract | ||
|---|---|---|
•A fully-automatic method for left ventricle segmentation from long-axis cine cardiac MR data is presented and extensively validated.•A combination of atlas-based and spatio-temporal registration approaches is used to accurately segment the left ventricle from cine MR sequence.•Usage of probabilistic tissue maps in the preprocessing step improves the multi-atlas-based segmentation accuracy.•Contour refinement steps that make use of local image intensity information further improves the segmentation accuracy.•Fully-automatic left ventricle segmentation from horizontal and vertical long-axis scans enables accurate and fast analysis of the cardiovascular function. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.media.2017.04.004 | Medical Image Analysis |
Keywords | Field | DocType |
Atlas-based segmentation,Registration,Cardiac MRI,Left ventricular segmentation,Long-axis cine MRI | Stroke volume,Population,Computer vision,Pearson product-moment correlation coefficient,Long axis,Automatic image annotation,Ejection fraction,Segmentation,Cardiac magnetic resonance,Artificial intelligence,Mathematics | Journal |
Volume | ISSN | Citations |
39 | 1361-8415 | 3 |
PageRank | References | Authors |
0.55 | 12 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rahil Khurram Shahzad | 1 | 7 | 2.07 |
| Qian Tao | 2 | 32 | 4.74 |
| Oleh Dzyubachyk | 3 | 189 | 18.59 |
| Marius Staring | 4 | 971 | 59.25 |
| B.P.F. Lelieveldt | 5 | 1331 | 115.59 |
| Rob J. Van Der Geest | 6 | 559 | 56.91 |