Name
Title | ||
|---|---|---|
3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation. |
Abstract | ||
|---|---|---|
Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In the isointense phase (approximately 6-8 months of age), white matter and gray matter exhibit similar levels of intensities in magnetic resonance (MR) images, due to the ongoing myelination and maturation. This results in extremely lo... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCYB.2018.2797905 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Image segmentation,Brain,Magnetic resonance imaging,Convolution,Solid modeling,Biomedical imaging | Convergence (routing),Normalization (statistics),Pattern recognition,Medical imaging,Convolution,Segmentation,Image segmentation,Solid modeling,Artificial intelligence,Mathematics,Machine learning,Speedup | Journal |
Volume | Issue | ISSN |
49 | 3 | 2168-2267 |
Citations | PageRank | References |
19 | 0.66 | 21 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dong Nie | 1 | 213 | 19.80 |
| Li Wang | 2 | 1051 | 78.25 |
| Ehsan Adeli Mosabbeb | 3 | 261 | 39.27 |
| Cuijin Lao | 4 | 19 | 0.66 |
| Weili Lin | 5 | 156 | 32.78 |
| Dinggang Shen | 6 | 7837 | 611.27 |