Name
Abstract | ||
|---|---|---|
Image segmentation based on convolutional neural networks is proving to be a powerful and efficient solution for medical applications. However, the lack of annotated data, presence of artifacts and variability in appearance can still result in inconsistencies during the inference. We choose to take advantage of the invariant nature of anatomical structures, by enforcing a semantic constraint to improve the robustness of the segmentation. The proposed solution is applied on a brain structures segmentation task, where the output of the network is constrained to satisfy a known adjacency graph of the brain regions. This criteria is introduced during the training through an original penalization loss named NonAdjLoss. With the help of a new metric, we show that the proposed approach significantly reduces abnormalities produced during the segmentation. Additionally, we demonstrate that our framework can be used in a semi-supervised way, opening a path to better generalization to unseen data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-00931-1_68 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Medical image segmentation,Convolutional neural network,Semi-supervised learning,Adjacency graph,Constraint | Adjacency list,Semi-supervised learning,Pattern recognition,Inference,Convolutional neural network,Segmentation,Computer science,Image segmentation,Robustness (computer science),Invariant (mathematics),Artificial intelligence | Conference |
Volume | ISSN | Citations |
11072 | 0302-9743 | 7 |
PageRank | References | Authors |
0.47 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pierre-Antoine Ganaye | 1 | 9 | 0.86 |
| Michaël Sdika | 2 | 81 | 9.17 |
| Hugues Benoit-Cattin | 3 | 187 | 22.17 |