Name
Abstract | ||
|---|---|---|
•A novel approach for 3D fully automatic and accurate breast tissue segmentation from MRI data is proposed.•Our approach avoids parameters explosion by using a suitably modified 2D deep U Net CNN model on 3D data.•The method relies on a multi planar combination of deep CNNs by a suitable projection fusing approach.•The proposal was evaluated by using two different datasets on a total of 109 MRI/DCE MRI studies with histopathologically proven lesions.•The proposal demonstrated to be able to cope with a multi protocol requirement. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.artmed.2019.101781 | Artificial Intelligence in Medicine |
Keywords | DocType | Volume |
MRI,Breast,Segmentation,Convolutional neural networks,U-Net | Journal | 103 |
ISSN | Citations | PageRank |
0933-3657 | 3 | 0.43 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Piantadosi | 1 | 10 | 4.33 |
| Mario Sansone | 2 | 14 | 3.97 |
| Roberta Fusco | 3 | 40 | 6.68 |
| C. Sansone | 4 | 1569 | 94.00 |