Name
Title | ||
|---|---|---|
Synthetic Magnetic Resonance Images for Domain Adaptation: Application to Fetal Brain Tissue Segmentation |
Abstract | ||
|---|---|---|
The quantitative assessment of the developing human brain in utero is crucial to fully understand neurodevelopment. Thus, automated multi-tissue fetal brain segmentation algorithms are being developed, which in turn require annotated data to be trained. However, the available annotated fetal brain datasets are limited in number and heterogeneity, hampering domain adaptation strategies for robust s... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ISBI52829.2022.9761451 | 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) |
Keywords | DocType | ISSN |
Image segmentation,Annotations,Magnetic resonance imaging,Transfer learning,Superresolution,Magnetic resonance,Grey matter | Conference | 1945-7928 |
ISBN | Citations | PageRank |
978-1-6654-2923-8 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Priscille de Dumast | 1 | 0 | 1.01 |
| Hamza Kebiri | 2 | 0 | 1.35 |
| Kelly Payette | 3 | 0 | 1.01 |
| Andras Jakab | 4 | 297 | 12.98 |
| Hélène Lajous | 5 | 0 | 0.34 |
| Meritxell Bach Cuadra | 6 | 326 | 23.59 |