Name
Abstract | ||
|---|---|---|
Dear Editor, In recent years, multi-modal medical image fusion has received widespread attention in the image processing community. However, existing works on medical image fusion methods are mostly devoted to pursuing high performance on visual perception and objective fusion metrics, while ignoring the specific purpose in clinical applications. In this letter, we propose a glioma segmentation-oriented multi-modal magnetic resonance (MR) image fusion method using an adversarial learning framework, which adopts a segmentation network as the discriminator to achieve more meaningful fusion results from the perspective of the segmentation task. Experimental results demonstrate the advantage of the proposed method over some state-of-the-art medical image fusion methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JAS.2022.105770 | IEEE/CAA Journal of Automatica Sinica |
Keywords | DocType | Volume |
meaningful fusion results,segmentation task,state-of-the-art medical image fusion methods,multimodal medical image fusion,image processing community,objective fusion metrics,glioma segmentation-oriented multimodal magnetic resonance image fusion method,adversarial learning framework,segmentation network | Journal | 9 |
Issue | ISSN | Citations |
8 | 2329-9266 | 0 |
PageRank | References | Authors |
0.34 | 17 | 5 |