Name
Abstract | ||
|---|---|---|
In this study, we propose a 3D deep neural network called U-ReSNet, a joint framework that can accurately register and segment medical volumes. The proposed network learns to automatically generate linear and elastic deformation models, trained by minimizing the mean square error and the local cross correlation similarity metrics. In parallel, a coupled architecture is integrated, seeking to provide segmentation maps for anatomies or tissue patterns using an additional decoder part trained with the dice coefficient metric. U-ReSNet is trained in an end to end fashion, while due to this joint optimization the generated network features are more informative leading to promising results compared to other deep learning-based methods existing in the literature. We evaluated the proposed architecture using the publicly available OASIS 3 dataset, measuring the dice coefficient metric for both registration and segmentation tasks. Our promising results indicate the potentials of our method which is composed from a convolutional architecture that is extremely simple and light in terms of parameters. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32248-9_35 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Image registration,Deformable registration,Brain tumor segmentation,3D convolutional neural networks | Conference | 11766 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.41 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Théo Estienne | 1 | 3 | 1.43 |
| Maria Vakalopoulou | 2 | 14 | 2.60 |
| S Christodoulidis | 3 | 160 | 10.20 |
| Enzo Battistela | 4 | 3 | 0.41 |
| Marvin Lerousseau | 5 | 3 | 1.76 |
| Alexandre Carre | 6 | 3 | 0.75 |
| Guillaume Klausner | 7 | 3 | 0.41 |
| Roger Sun | 8 | 3 | 1.09 |
| Charlotte Robert | 9 | 3 | 1.43 |
| Stavroula G Mougiakakou | 10 | 342 | 28.61 |
| Nikos Paragios | 11 | 6055 | 387.68 |
| Deutsch Eric W | 12 | 102 | 10.94 |