Name
Abstract | ||
|---|---|---|
In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1117/12.2081777 | Proceedings of SPIE |
Keywords | Field | DocType |
Fetal MRI,Brain Extraction,Template-based Segmentation,Multi-Atlas Fusion | Brain segmentation,Computer vision,Scale-space segmentation,Brain mri,Segmentation,Image segmentation,Atlas (anatomy),Preprocessor,Artificial intelligence,Physics | Conference |
Volume | ISSN | Citations |
9413 | 0277-786X | 3 |
PageRank | References | Authors |
0.37 | 11 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sébastien Tourbier | 1 | 19 | 1.40 |
| P. Hagmann | 2 | 511 | 35.38 |
| m cagneaux | 3 | 3 | 0.37 |
| L. Guibaud | 4 | 3 | 0.71 |
| subrahmanyam gorthi | 5 | 19 | 1.79 |
| Marie Schaer | 6 | 62 | 4.87 |
| Jean-Philippe Thiran | 7 | 2320 | 257.56 |
| Reto Meuli | 8 | 296 | 107.65 |
| Meritxell Bach Cuadra | 9 | 326 | 23.59 |