scholarly journals Non-Local U-Nets for Biomedical Image Segmentation

2020 ◽  
Vol 34 (04) ◽  
pp. 6315-6322
Author(s):  
Zhengyang Wang ◽  
Na Zou ◽  
Dinggang Shen ◽  
Shuiwang Ji
Keyword(s):  
Image Segmentation ◽  
Great Promise ◽  
Biomedical Image ◽  
Decoder Architecture ◽  
Efficiency And Effectiveness ◽  
Local Operators ◽  
Non Local ◽  
Infant Brain ◽  
Segmentation Task ◽  
Mr Image Segmentation

Deep learning has shown its great promise in various biomedical image segmentation tasks. Existing models are typically based on U-Net and rely on an encoder-decoder architecture with stacked local operators to aggregate long-range information gradually. However, only using the local operators limits the efficiency and effectiveness. In this work, we propose the non-local U-Nets, which are equipped with flexible global aggregation blocks, for biomedical image segmentation. These blocks can be inserted into U-Net as size-preserving processes, as well as down-sampling and up-sampling layers. We perform thorough experiments on the 3D multimodality isointense infant brain MR image segmentation task to evaluate the non-local U-Nets. Results show that our proposed models achieve top performances with fewer parameters and faster computation.

scholarly journals Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks

2019 ◽  
Vol 33 ◽  
pp. 8417-8424 ◽  
Author(s):  
Xiang He ◽  
Sibei Yang ◽  
Guanbin Li ◽  
Haofeng Li ◽  
Huiyou Chang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
State Of The Art ◽  
Contextual Information ◽  
Local Context ◽  
Deep Convolutional Neural Networks ◽  
Biomedical Image ◽  
Feature Representations ◽  
Segmentation Methods ◽  
Spatial Dependencies ◽  
Non Local

Recent progress in biomedical image segmentation based on deep convolutional neural networks (CNNs) has drawn much attention. However, its vulnerability towards adversarial samples cannot be overlooked. This paper is the first one that discovers that all the CNN-based state-of-the-art biomedical image segmentation models are sensitive to adversarial perturbations. This limits the deployment of these methods in safety-critical biomedical fields. In this paper, we discover that global spatial dependencies and global contextual information in a biomedical image can be exploited to defend against adversarial attacks. To this end, non-local context encoder (NLCE) is proposed to model short- and long-range spatial dependencies and encode global contexts for strengthening feature activations by channel-wise attention. The NLCE modules enhance the robustness and accuracy of the non-local context encoding network (NLCEN), which learns robust enhanced pyramid feature representations with NLCE modules, and then integrates the information across different levels. Experiments on both lung and skin lesion segmentation datasets have demonstrated that NLCEN outperforms any other state-of-the-art biomedical image segmentation methods against adversarial attacks. In addition, NLCE modules can be applied to improve the robustness of other CNN-based biomedical image segmentation methods.

scholarly journals Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation

NeuroImage ◽  
2014 ◽  
Vol 89 ◽  
pp. 152-164 ◽  
Author(s):  
Li Wang ◽  
Feng Shi ◽  
Yaozong Gao ◽  
Gang Li ◽  
John H. Gilmore ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Mr Image ◽  
Infant Brain ◽  
Mr Image Segmentation

scholarly journals A Self-Spatial Adaptive Weighting Based U-Net for Image Segmentation

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 348
Author(s):  
Choongsang Cho ◽  
Young Han Lee ◽  
Jongyoul Park ◽  
Sangkeun Lee
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Feature Maps ◽  
Data Set ◽  
Feature Map ◽  
Adaptive Weighting ◽  
Spatially Adaptive ◽  
Wide Range ◽  
Decoder Architecture

Semantic image segmentation has a wide range of applications. When it comes to medical image segmentation, its accuracy is even more important than those of other areas because the performance gives useful information directly applicable to disease diagnosis, surgical planning, and history monitoring. The state-of-the-art models in medical image segmentation are variants of encoder-decoder architecture, which is called U-Net. To effectively reflect the spatial features in feature maps in encoder-decoder architecture, we propose a spatially adaptive weighting scheme for medical image segmentation. Specifically, the spatial feature is estimated from the feature maps, and the learned weighting parameters are obtained from the computed map, since segmentation results are predicted from the feature map through a convolutional layer. Especially in the proposed networks, the convolutional block for extracting the feature map is replaced with the widely used convolutional frameworks: VGG, ResNet, and Bottleneck Resent structures. In addition, a bilinear up-sampling method replaces the up-convolutional layer to increase the resolution of the feature map. For the performance evaluation of the proposed architecture, we used three data sets covering different medical imaging modalities. Experimental results show that the network with the proposed self-spatial adaptive weighting block based on the ResNet framework gave the highest IoU and DICE scores in the three tasks compared to other methods. In particular, the segmentation network combining the proposed self-spatially adaptive block and ResNet framework recorded the highest 3.01% and 2.89% improvements in IoU and DICE scores, respectively, in the Nerve data set. Therefore, we believe that the proposed scheme can be a useful tool for image segmentation tasks based on the encoder-decoder architecture.

SEAUNet:Domain Adaptation for Biomedical Image Segmentation

2020 ◽  
Author(s):  
Hang Chen ◽  
Leiting Chen ◽  
Yuchu Chen ◽  
Minghao Fan ◽  
Chuan Zhou
Keyword(s):  
Image Segmentation ◽  
Biomedical Image
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