Name
Abstract | ||
|---|---|---|
The segmentation of brain tissue in MRI is valuable for extracting brain structure to aid diagnosis, treatment and tracking the progression of different neurologic diseases. Medical image data are volumetric and some neural network models for medical image segmentation have addressed this using a 3D convolutional architecture. However, this volumetric spatial information has not been fully exploited to enhance the representative ability of deep networks, and these networks have not fully addressed the practical issues facing the analysis of multimodal MRI data. In this paper, we propose a spatially-weighted 3D network (SW-3D-UNet) for brain tissue segmentation of single-modality MRI, and extend it using multimodality MRI data. We validate our model on the MRBrainS13 and MALC12 datasets. This unpublished model ranked first on the leaderboard of the MRBrainS13 Challenge. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TMI.2019.2937271 | IEEE transactions on medical imaging |
Keywords | DocType | Volume |
Magnetic resonance imaging,Three-dimensional displays,Image segmentation,Brain modeling,Hidden Markov models | Journal | 39 |
Issue | ISSN | Citations |
4 | 0278-0062 | 3 |
PageRank | References | Authors |
0.41 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liyan Sun | 1 | 11 | 2.22 |
| Wenao Ma | 2 | 6 | 1.79 |
| Xinghao Ding | 3 | 591 | 52.95 |
| Yue Huang | 4 | 35 | 6.24 |
| Dong Liang | 5 | 47 | 10.50 |
| John Paisley | 6 | 54 | 4.63 |