Name
Title | ||
|---|---|---|
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation. |
Abstract | ||
|---|---|---|
•An efficient 11-layers deep, multi-scale, 3D CNN architecture.•A novel training strategy that significantly boosts performance.•The first employment of a 3D fully connected CRF for post-processing.•State-of-the-art performance on three challenging lesion segmentation tasks.•New insights into the automatically learned intermediate representations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.media.2016.10.004 | Medical Image Analysis |
Keywords | DocType | Volume |
3D convolutional neural network,Fully connected CRF,Segmentation,Brain lesions,Deep learning | Journal | 36 |
ISSN | Citations | PageRank |
1361-8415 | 281 | 10.17 |
References | Authors | |
44 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Konstantinos Kamnitsas | 1 | 361 | 15.18 |
| Christian Ledig | 2 | 489 | 27.08 |
| Virginia F. J. Newcombe | 3 | 281 | 10.84 |
| Joanna P. Simpson | 4 | 281 | 10.17 |
| Andrew D. Kane | 5 | 281 | 10.17 |
| David K Menon | 6 | 321 | 16.59 |
| Daniel Rueckert | 7 | 9338 | 637.58 |
| Ben Glocker | 8 | 2157 | 119.81 |