MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

@article{Chen2020MMFNetAM,
  title={MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma},
  author={Huai Chen and Yuxiao Qi and Yong Yin and TengXiang Li and Guanzhong Gong and Lisheng Wang},
  journal={Neurocomputing},
  year={2020},
  volume={394},
  pages={27-40}
}
Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance Images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet) based on three modalities of MRI (T1, T2 and contrast-enhanced T1) is proposed to complete accurate segmentation of NPC. The… Expand
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References

SHOWING 1-10 OF 93 REFERENCES
Tumor segmentation via multi-modality joint dictionary learning
TLDR
This paper proposes a multi-modality joint dictionary learning method for NPC tumor segmentation that outperforms the benchmark method and achieves comparable results with prior NPC segmentation methods. Expand
A discriminative learning based approach for automated nasopharyngeal carcinoma segmentation leveraging multi-modality similarity metric learning
  • Zongqing Ma, Xi Wu, +4 authors Jiliu Zhou
  • Computer Science
  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
  • 2018
TLDR
This paper proposes a discriminative learning based approach for automated nasopharyngeal carcinoma segmentation using multi-modality images and achieves improved segmentation performance compared to its counterpart without multi- modality similarity metric learning and the segmentation method of solely using CT. Expand
Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut
TLDR
A novel automated NPC segmentation method in magnetic resonance (MR) images is proposed by combining a deep convolutional neural network (CNN) model and a 3D graph cut-based method in a two-stage manner and demonstrated that the proposed method is effective and accurate for NPCs segmentation. Expand
A review: Deep learning for medical image segmentation using multi-modality fusion
TLDR
The general principle of deep learning and multi-modal medical image segmentation is introduced, and different deep learning network architectures are presented, then analyzed to analyze their fusion strategies and compare their results. Expand
Automatic Tumor Segmentation with Deep Convolutional Neural Networks for Radiotherapy Applications
TLDR
Experimental results show that the proposed method outperforms the traditional hand-crafted features based segmentation methods, and could be useful for NPC diagnosis and helpful for guiding radiotherapy. Expand
Region-Based Nasopharyngeal Carcinoma Lesion Segmentation from MRI Using Clustering- and Classification-Based Methods with Learning
TLDR
Two region-based methods with parameters learning are introduced for NPC segmentation and demonstrate the superiority of adopting learning in the two introduced methods, which achieve comparable segmentation performance from a statistical point of view. Expand
Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI
TLDR
Results demonstrate that information across modalities can in particular improve performance on varying structures such as the spleen, and show that multi-modal learning can improve overall accuracy over modality-specific training. Expand
MRI Tumor Segmentation for Nasopharyngeal Carcinoma Using Knowledge-based Fuzzy Clustering
TLDR
Visual evaluation on MR images of NPC patients showed that KBFC achieved better tumor segmentation results than seeds growing and maximal likelihood method (MLM), compared with ground truth (GT). Expand
A Texture Combined Multispectral Magnetic Resonance Imaging Segmentation for Nasopharyngeal Carcinoma
TLDR
A texture combined multispectral fuzzy clustering (TCMFC) segmentation algorithm was proposed and showed that by reducing the false positives significantly, the TCMFC method achieved improved segmentation results, compared with the GM method. Expand
Automatic nasopharyngeal carcinoma segmentation in MR images with convolutional neural networks
  • Zongqing Ma, Xi Wu, Jiliu Zhou
  • Computer Science
  • 2017 International Conference on the Frontiers and Advances in Data Science (FADS)
  • 2017
TLDR
A novel automatic nasopharyngeal carcinoma segmentation method using deep Convolutional Neural Network (CNN), where three deep single-view CNNs are trained separately on patches extracted from axial, sagittal and coronal view respectively, then their predicted classification information are integrated for NPC segmentation. Expand
...
1
2
3
4
5
...