在医学图象中，多模态数据因成像机理不同而能从多种层面提供信息。多模态图像分割包含重点问题为如何融合(fusion)不同模态间信息，本文主要记录笔者最近所读，欢迎批评指正补充
1. A review: Deep learning for medical image segmentation using multi-modality fusion (Array 2019）***
融合策略分类
综述，按照方法的位置将融合策略分为三大类：Input-level, Layer-level, Decision-level.
数据集：
几种多模态数据集
2. Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer (TMI 2019）****
Abstract : Layer-level fusion, U-net, PET-CT, Lung cancer
Method : 对CT与PET各一个encoder，不同层feature stack后经conv得到权重，与concat后的feature点积，得加权feature map。
Experiment : 肺癌数据，对比layer-level fusion : MB(multi-branch), MC(multi-channel), FS(fused)，效果不错。
3. 3D FULLY CONVOLUTIONAL NETWORKS FOR CO-SEGMENTATION OF TUMORS ON PET-CT IMAGES (ISBI 2018）**
Abstract : Decision-level, Unet, Graph cut, PET-CT, Lung cancer
Method : CT, PET独立的Net，各输出概率图后Graph Cut。
Experiment : 肺癌数据，对比graph-cut。
4. HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation (TMI 2019) ****
Abstract : Layer-level, DenseNet, MRI, Brain
Method : modality各一个net, 中间层相互dense连接。
Experiment : Brain(iseg-2017, MRBrainS)，对比layer level fusion : Single Dense Path, Dual Dense Path, Disentangled modalities with early fusion。各模态先经卷积再拼接相比直接双通道输入有较明显提升。
5. Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancers (MIDL 2019) *
Abstract : Input-level, Unet, Pet-CT, head and neck
Method : 2通道输入
Experiment :头颈部肿瘤， 对比单模态Unet，是HECKTOR比赛数据来源。
6. Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network (Physics in Medicine & Biology 2019) **
Abstract : Layer-level, V-net, Pet-CT, Lung cancer
Method : 2个V-net 先分别提取PET/CT feature，sum后经4层卷积得result. 提出weighted cross entropy loss以balance不同模态影响。
Experiment : 肺癌数据，对比其它几种fusion方法，传统方法，单模态V-net。
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来源：oschina
链接：https://my.oschina.net/u/4275057/blog/4296557