Name
Abstract | ||
|---|---|---|
Algorithms for fusing information acquired from different imaging modalities have shown to improve the segmentation results of various applications in the medical field. Motivated by recent successes achieved using densely connected fusion networks, we propose a new fusion architecture for the purpose of 3D segmentation in multi-modal brain MRI volumes. Based on a hyper-densely connected convolutional neural network, our network features in promoting a progressive information abstraction process, introducing a new module - ResFuse to merge and normalize features from different modalities and adopting combo loss for handing data imbalances. The proposed approach is evaluated on both an outsourced dataset for acute ischemic stroke lesion segmentation and a public dataset for infant brain segmentation (iSeg-17). The experiment results show our approach achieves superior performances for both datasets compared to the state-of-art fusion network. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-87586-2_9 | MACHINE LEARNING IN CLINICAL NEUROIMAGING |
Keywords | DocType | Volume |
Multi-modal fusion, Dense network, Brain segmentation | Conference | 13001 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenting Duan | 1 | 0 | 0.34 |
| Lei Zhang | 2 | 15 | 2.91 |
| Jordan Colman | 3 | 5 | 0.79 |
| Giosue Gulli | 4 | 0 | 0.34 |
| Xujiong Ye | 5 | 14 | 4.67 |