scholarly journals Automatic lumbar spinal MRI image segmentation with a multi-scale attention network

2021 ◽  
Author(s):  
Haixing Li ◽  
Haibo Luo ◽  
Wang Huan ◽  
Zelin Shi ◽  
Chongnan Yan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Spinal Mri ◽  
Lumbar Spinal ◽  
Mri Image

scholarly journals MRI Image Segmentation of Nasopharyngeal Carcinoma Using Multi-Scale Cascaded Fully Convolutional Network

2022 ◽  
Vol 31 (3) ◽  
pp. 1771-1782
Author(s):  
Yanfen Guo ◽  
Zhe Cui ◽  
Xiaojie Li ◽  
Jing Peng ◽  
Jinrong Hu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Nasopharyngeal Carcinoma ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Multi Scale ◽  
Mri Image

MC-Net: multi-scale context-attention network for medical CT image segmentation

2021 ◽  
Author(s):  
Haiying Xia ◽  
Mingjun Ma ◽  
Haisheng Li ◽  
Shuxiang Song
Keyword(s):  
Image Segmentation ◽  
Ct Image ◽  
Attention Network ◽  
Multi Scale

2D Medical Image Segmentation via Learning Multi-scale Contextual Dependencies

Methods ◽  
2021 ◽  
Author(s):  
Shuchao Pang ◽  
Anan Du ◽  
Zhenmei Yu ◽  
Mehmet A. Orgun
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Multi Scale

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

scholarly journals Chicken Image Segmentation via Multi-Scale Attention-Based Deep Convolutional Neural Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Wei Li ◽  
Yang Xiao ◽  
Xibin Song ◽  
Na Lv ◽  
Xinbo Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Multi Scale
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