In this paper, we introduce an adaptive multi-modality 3D medical image segmentation network based on Transformer (called msFormer), which is also a powerful 3D fusion network, and extend the application of Transformer to multi-modality medical image segmentation. This fusion network is modeled in...
Deep learning Medical image segmentation Multi-modality fusion Review 1. Introduction Segmentation using multi-modality has been widely studied with the development of medical image acquisition systems. Different strategies for image fusion, such as probability theory [1], [2], fuzzy concept [3], [...
为此,作者提出了 Modality-agnostic Domain Generalizable Network (MADGNet),该网络主要包括多频率多尺度注意力块(MFMSA)和集成子解码模块(ESDM),分别用于结合多频率和多尺度特征来改进空间特征提取和减少深度监督下多任务学习中的信息丢失。 网络结构 上图是作者提出的 MADGNet 的总体结构图,整体采用了U-Net的网络...
Image analysis using more than one modality (i.e. multi-modal) has been increasingly applied in the field of biomedical imaging. One of the challenges in performing the multimodal analysis is that there exist multiple schemes for fusing the information from different modalities, where such schemes...
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...
第五个挑战是在模态的表示和它们的预测模型之间转移知识。协同学习探索了从一种模态中学习的知识如何帮助在不同模态上训练的计算模型。当其中一种模式的资源有限(例如,带注释的数据)时,这一挑战尤其重要。辅助模态(helper modality)通常只参与模型的训练过程,并不参与模型的测试使用过程 ...
Image Registration (图像配准) Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration. [Paper] [Oral & Best Paper Candidate!!!] Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration. [Paper][Code] ...
For the downstream machine-learning task, we focus on the whole-tumor segmentation. From Table 3 and Fig. 3, MM-DSL achieves higher performance than the baselines of FLGAN, FedMed-GAN, and AsynDGAN on multi-modality image generation and whole-tumor segmentation on the BraTS dataset. The ...
Cross-Modality Multi-Atlas Segmentation Using Deep Neural Networks Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical ima... W Ding,L Li,X Zhuang,... 被引量: 0发表: 2022年 Automated multi-mo...
The proposed approach comprises a hierarchy that contains focus detection, initial segmentation, consistency verification and fusion. The objective of theutilization of the CNN is to generate a focus map from the two input images. The focus map is hanged to a binary map. Some sort ofimage post...