Name
Abstract | ||
|---|---|---|
Segmentation of organs in medical images can be successfully performed with shape-constrained deformable models. A surface mesh is attracted to detected image boundaries by an external energy, while an internal energy keeps the mesh similar to expected shapes. Complex organs like the heart with its four chambers can be automatically segmented using a suitable shape variability model based on piecewise affine degrees of freedom. In this paper, we extend the approach to also segment highly variable vascular structures. We introduce a dedicated framework to adapt an extended mesh model to freely bending vessels. This is achieved by subdividing each vessel into (short) tube-shaped segments ("tubelets"). These are assigned to individual similarity transformations for local orientation and scaling. Proper adaptation is achieved by progressively adapting distal vessel parts to the image only after proximal neighbor tubelets have already converged. In addition, each newly activated tubelet inherits the local orientation and scale of the preceeding one. To arrive at a joint segmentation of chambers and vasculature, we extended a previous model comprising endocardial surfaces of the four chambers, the left ventricular epicardium, and a pulmonary artery trunk. Newly added are the aorta (ascending and descending plus arch), superior and inferior vena cava, coronary sinus, and four pulmonary veins. These vessels are organized as stacks of triangulated rings. This mesh configuration is most suitable to define tubelet segments. On 36 CT data sets reconstructed at several cardiac phases from 17 patients, segmentation accuracies of 0.61-0.80 mm are obtained for the cardiac chambers. For the visible parts of the newly added great vessels, surface accuracies of 0.47-1.17 mm are obtained (larger errors are associated with faintly contrasted venous structures). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1117/12.768494 | PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) |
Keywords | Field | DocType |
image segmentation,vessel segmentation,progressive segmentation,cardiovascular modeling,shape-constrained deformable models,tubelet models | Biomedical engineering,Anatomy,Data set,Left Ventricular Epicardium,Segmentation,Computer science,Great vessels,Image segmentation,Coronary sinus,Piecewise,Inferior vena cava | Conference |
Volume | ISSN | Citations |
6914 | 0277-786X | 11 |
PageRank | References | Authors |
1.09 | 9 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jochen Peters | 1 | 284 | 25.51 |
| Olivier Ecabert | 2 | 346 | 26.28 |
| Christine H. Lorenz | 3 | 36 | 5.14 |
| Jens Von Berg | 4 | 247 | 27.11 |
| Matthew J. Walker | 5 | 65 | 5.52 |
| Thomas Ivanc | 6 | 43 | 2.57 |
| M Vembar | 7 | 51 | 7.27 |
| m olszewski | 8 | 13 | 1.54 |
| j s weese | 9 | 11 | 1.09 |