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

arXiv: https://arxiv.org/abs/1812.10033 · HTML: https://mlsanity.com/paper/1812.10033/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2018.

## Built on by (most cited first, 8 of 8)

- [TransMed: Transformers Advance Multi-modal Medical Image Classification](https://mlsanity.com/paper/2103.05940/) (2021)
- [ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst](https://mlsanity.com/paper/2305.16103/) (2023)
- [Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals](https://mlsanity.com/paper/2309.13401/) (2023)
- [Automatic Segmentation of Gross Target Volume of Nasopharynx Cancer using Ensemble of Multiscale Deep Neural Networks with Spatial Attention](https://mlsanity.com/paper/2101.11254/) (2021)
- [A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation](https://mlsanity.com/paper/2404.03253/) (2024)
- [OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation](https://mlsanity.com/paper/2307.15588/) (2023)
- [Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis](https://mlsanity.com/paper/2201.09421/) (2022)
- [Leveraging Semantic Asymmetry for Precise Gross Tumor Volume Segmentation of Nasopharyngeal Carcinoma in Planning CT](https://mlsanity.com/paper/2411.18290/) (2024)

## Abstract (arXiv)

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.