تقرير
MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma
العنوان: | MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma |
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المؤلفون: | Chen, Huai, Qi, Yuxiao, Yin, Yong, Li, Tengxiang, Liu, Xiaoqing, Li, Xiuli, Gong, Guanzhong, Wang, Lisheng |
سنة النشر: | 2018 |
المجموعة: | Computer Science |
مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
الوصف: | 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 backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to capture modality-specific features and one single decoder to fuse them and obtain high-level features for NPC segmentation. A fusion block is presented to effectively fuse features from multi-modality MRI. It firstly recalibrates low-level features captured from modality-specific encoders to highlight both informative features and regions of interest, then fuses weighted features by a residual fusion block to keep balance between fused ones and high-level features from decoder. Moreover, a training strategy named self-transfer, which utilizes pre-trained modality-specific encoders to initialize multi-encoder-based network, is proposed to make full mining of information from different modalities of MRI. The proposed method based on multi-modality MRI can effectively segment NPC and its advantages are validated by extensive experiments. Comment: 34 pages, 12 figures |
نوع الوثيقة: | Working Paper |
URL الوصول: | http://arxiv.org/abs/1812.10033 |
رقم الأكسشن: | edsarx.1812.10033 |
قاعدة البيانات: | arXiv |
الوصف غير متاح. |