Name
Abstract | ||
|---|---|---|
Non-linear registration is an essential step in neuroimaging, influencing both structural and functional analyses. Although important, how different registration methods influence the results of these analyses is poorly known, with the metrics used to compare methods weakly justified. In this work we propose a framework to simulate true deformation fields derived from manually segmented volumes of interest. We test both state-of-the-art binary and non-binary, volumetric and surface -based metrics against these true deformation fields. Our results show that surface-based metrics are twice as sensitive as volume-based metrics, but are typically less used in non-linear registration evaluations. All analysed metrics poorly explained the true deformation field, with none explaining more than half the variance. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-24571-3_47 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Nonlinear system,Pattern recognition,Computer science,Artificial intelligence,Neuroimaging,Binary number | Conference | 9350 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.37 |
References | Authors | |
8 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andre Santos Ribeiro | 1 | 2 | 0.37 |
| David J. Nutt | 2 | 27 | 4.57 |
| John McGonigle | 3 | 4 | 0.77 |