Name
Abstract | ||
|---|---|---|
Recent advances in supervised deep learning, mainly using convolutional neural networks, enabled the fast acquisition of high-quality brain tissue segmentation from structural magnetic resonance brain images (MRI). However, the robustness of such deep learning models is limited by the existing training datasets acquired with a homogeneous MRI acquisition protocol. Moreover, current models fail to utilize commonly available relevant non-imaging information (i.e., meta-data). In this paper, the notion of a braided block is introduced as a generalization of convolutional or fully connected layers for learning from paired data (meta-data, images). For robust MRI tissue segmentation, a braided 3D U-Net architecture is implemented as a combination of such braided blocks with scanner information, MRI sequence parameters, geometrical information, and task-specific prior information used as meta-data. When applied … |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISBI45749.2020.9098601 | ISBI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mahmoud Mostapha | 1 | 0 | 2.37 |
| Boris Mailhe | 2 | 0 | 0.34 |
| X. Chen | 3 | 2 | 2.07 |
| Pascal Ceccaldi | 4 | 0 | 0.34 |
| Youngjin Yoo | 5 | 122 | 9.07 |
| Mariappan Nadar | 6 | 0 | 0.34 |