Name
Abstract | ||
|---|---|---|
A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D +time structures, such as the cardiac left ventricle, due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the “curse of dimensionality.” Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo- and epicardium contours of the left ventricle by coupling them at segmentation-time, instead of at model-time. We apply our approach to 33 3D+time cardiac MRI datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TMI.2010.2086465 | IEEE Transactions on Medical Imaging |
Keywords | Field | DocType |
biomedical MRI,cardiology,image segmentation,medical image processing,3D+time cardiac MRI datasets,3D+time segmentation technique,alternate bottom-up modelling approach,cardiac left ventricle,flexible contour coupling technique,left ventricle endocardium contour,left ventricle epicardium contour,left ventricular segmentation technique,model based segmentation technique,multiple dimensionally targeted models,scalable segmentation framework,segmentation accuracy,unseen contour configurations,ASM optimization,Active shape model (ASM),contour coupling,spatio-temporal left ventricle dynamics | Training set,Computer vision,Scale-space segmentation,Segmentation,Maxima and minima,Image segmentation,Artificial intelligence,Solid modeling,Mathematics,Scalability | Journal |
Volume | Issue | ISSN |
30 | 2 | 0278-0062 |
Citations | PageRank | References |
12 | 0.54 | 34 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stephen P. O'Brien | 1 | 12 | 0.54 |
| Ovidiu Ghita | 2 | 234 | 18.12 |
| Paul F. Whelan | 3 | 561 | 39.95 |