Name
Abstract | ||
|---|---|---|
A new data structure is presented for geometrically modeling multi-objects. The model can exhibit elastic and fluid-like behavior to enable interpretability between tasks that require both deformable registration and active contour segmentation. The data structure consists of a label mask, distance field, and springls (a constellation of disconnected triangles). The representation has sub-voxel precision, is parametric, re-meshes, tracks point correspondences, and guarantees no self-intersections, air-gaps, or overlaps between adjacent structures. In this work, we show how to apply existing registration algorithms and active contour segmentation to the data structure; and as a demonstration, the data structure is used to segment cortical and subcortical structures (74 total) in the human brain. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33415-3_61 | MICCAI |
Keywords | Field | DocType |
tracking,level set,active contour,segmentation | Active contour model,Computer vision,Interpretability,Data structure,Pattern recognition,Computer science,Segmentation,Level set,Software,Distance transform,Parametric statistics,Artificial intelligence | Conference |
Volume | Issue | ISSN |
15 | Pt 1 | 0302-9743 |
Citations | PageRank | References |
4 | 0.39 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Blake C. Lucas | 1 | 52 | 5.50 |
| Michael Kazhdan | 2 | 2940 | 140.03 |
| Russell H. Taylor | 3 | 1970 | 438.00 |