A review: Deep learning for medical image segmentation using multi-modality fusion
Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes · Université de Rouen Normandie · +1 more institution
Abstract
Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve the segmentation. Recently, deep learning-based approaches have presented the state-of-the-art performance in image classification, segmentation, object detection and tracking tasks. Due to their self-learning and generalization ability over large amounts of data, deep learning recently has also gained great interest in multi-modal medical image segmentation. In this paper, we give an overview of deep learning-based approaches for multi-modal medical image segmentation task. Firstly, we introduce the…
Citation impact
- FWCI
- 33.65
- Percentile
- 100%
- References
- 136
Authors
3- TZTongxue ZhouCorresponding
Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, Université de Rouen Normandie, Institut National des Sciences Appliquées Rouen Normandie
- SRSu Ruan
Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, Université de Rouen Normandie
- SCStéphane Canu
Laboratoire d'Informatique, du Traitement de l'Information et des Systèmes, Institut National des Sciences Appliquées Rouen Normandie
Topics & keywords
- Segmentation
- Artificial intelligence
- Deep learning
- Computer science
- Modality (human–computer interaction)
- Image segmentation
- Generalization
- Modalities