Name
Title | ||
|---|---|---|
Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation. |
Abstract | ||
|---|---|---|
•Automatic organ segmentation in 3D medical scans is an important yet challenging problem for medical image analysis, especially the pancreas.•As a solution, we present an automated system based on a two-stage cascaded approach: pancreas localization and pancreas segmentation.•We design a complete deep-learning approach based on efficient holistically-nested convolutional networks applied to three orthogonal views.•Quantitative evaluation on a public CT dataset of 82 patients shows state-of-the art performance with 81.27 ± 6.27% Dice score in validation. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.media.2018.01.006 | Medical Image Analysis |
Keywords | DocType | Volume |
Fully convolutional networks,Holistically nested networks,Deep learning,Medical imaging,Computed tomography,Pancreas segmentation | Journal | 45 |
ISSN | Citations | PageRank |
1361-8415 | 26 | 1.67 |
References | Authors | |
51 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Holger Roth | 1 | 737 | 45.70 |
| Le Lu | 2 | 1297 | 86.78 |
| Nathan Lay | 3 | 59 | 6.19 |
| Adam P. Harrison | 4 | 101 | 17.06 |
| Amal Farag | 5 | 195 | 8.57 |
| Andrew Sohn | 6 | 242 | 27.86 |
| Ronald M. Summers | 7 | 893 | 86.16 |