Name
Title | ||
|---|---|---|
Multilabel, Multiscale Topological Transformation for Cerebral MRI Segmentation Post-processing. |
Abstract | ||
|---|---|---|
Accurate segmentation of cerebral structures remains, after two decades of research, a complex task. In particular, obtaining satisfactory results in terms of topology, in addition to quantitative and geometrically correct properties is still an ongoing issue. In this paper, we investigate how recent advances in multilabel topology and homotopy-type preserving transformations can be involved in the development of multiscale topological modelling of brain structures, and topology-based post-processing of segmentation maps of brain MR images. In this context, a preliminary study and a proof-of-concept are presented. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20867-7_36 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Homotopic deformation,Multilabel topology,Multiscale modelling,Segmentation,MRI,Brain | Topology,Computer science,Segmentation | Conference |
Volume | ISSN | Citations |
11564 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Tor-Díez | 1 | 0 | 0.34 |
| Sylvain Faisan | 2 | 32 | 6.99 |
| Loïc Mazo | 3 | 37 | 5.79 |
| Nathalie Bednarek | 4 | 10 | 2.22 |
| Hélène Meunier | 5 | 0 | 1.01 |
| Isabelle Bloch | 6 | 2123 | 170.75 |
| Nicolas Passat | 7 | 469 | 46.22 |
| François Rousseau | 8 | 346 | 22.15 |