scholarly journals Magnetic Resonance Image Segmentation Based on Multi-Scale Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 65758-65768
Author(s):  
Jinglong Hao ◽  
Xiaoxi Li ◽  
Yanxia Hou
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Magnetic Resonance Image ◽  
Multi Scale

scholarly journals Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jinling Zhang ◽  
Jun Yang ◽  
Min Zhao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Liver Cancer ◽  
Convolutional Neural Network ◽  
Cancer Patients ◽  
Segmentation Algorithm ◽  
Image Segmentation Algorithm ◽  
Single Sequence ◽  
Mri Image

To study the influence of different sequences of magnetic resonance imaging (MRI) images on the segmentation of hepatocellular carcinoma (HCC) lesions, the U-Net was improved. Moreover, deep fusion network (DFN), data enhancement strategy, and random data (RD) strategy were introduced, and a multisequence MRI image segmentation algorithm based on DFN was proposed. The segmentation experiments of single-sequence MRI image and multisequence MRI image were designed, and the segmentation result of single-sequence MRI image was compared with those of convolutional neural network (FCN) algorithm. In addition, RD experiment and single-input experiment were also designed. It was found that the sensitivity (0.595 ± 0.145) and DSC (0.587 ± 0.113) obtained by improved U-Net were significantly higher than the sensitivity (0.405 ± 0.098) and DSC (0.468 ± 0.115, P < 0.05 ) obtained by U-Net. The sensitivity of multisequence MRI image segmentation algorithm based on DFN (0.779 ± 0.015) was significantly higher than that of FCN algorithm (0.604 ± 0.056, P < 0.05 ). The multisequence MRI image segmentation algorithm based on the DFN had higher indicators for liver cancer lesions than those of the improved U-Net. When RD was added, it not only increased the DSC of the single-sequence network enhanced by the hepatocyte-specific magnetic resonance contrast agent (Gd-EOB-DTPA) by 1% but also increased the DSC of the multisequence MRI image segmentation algorithm based on DFN by 7.6%. In short, the improved U-Net can significantly improve the recognition rate of small lesions in liver cancer patients. The addition of RD strategy improved the segmentation indicators of liver cancer lesions of the DFN and can fuse image features of multiple sequences, thereby improving the accuracy of lesion segmentation.

scholarly journals Multi-Scale and Multi-Branch Convolutional Neural Network for Retinal Image Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 365
Author(s):  
Yun Jiang ◽  
Wenhuan Liu ◽  
Chao Wu ◽  
Huixiao Yao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Retinal Image ◽  
Spatial Information ◽  
Multi Scale ◽  
Segmentation Methods ◽  
Feature Information ◽  
The Impact ◽  
Retinal Image Segmentation

The accurate segmentation of retinal images is a basic step in screening for retinopathy and glaucoma. Most existing retinal image segmentation methods have insufficient feature information extraction. They are susceptible to the impact of the lesion area and poor image quality, resulting in the poor recovery of contextual information. This also causes the segmentation results of the model to be noisy and low in accuracy. Therefore, this paper proposes a multi-scale and multi-branch convolutional neural network model (multi-scale and multi-branch network (MSMB-Net)) for retinal image segmentation. The model uses atrous convolution with different expansion rates and skip connections to reduce the loss of feature information. Receiving domains of different sizes captures global context information. The model fully integrates shallow and deep semantic information and retains rich spatial information. The network embeds an improved attention mechanism to obtain more detailed information, which can improve the accuracy of segmentation. Finally, the method of this paper was validated on the fundus vascular datasets, DRIVE, STARE and CHASE datasets, with accuracies/F1 of 0.9708/0.8320, 0.9753/0.8469 and 0.9767/0.8190, respectively. The effectiveness of the method in this paper was further validated on the optic disc visual cup DRISHTI-GS1 dataset with an accuracy/F1 of 0.9985/0.9770. Experimental results show that, compared with existing retinal image segmentation methods, our proposed method has good segmentation performance in all four benchmark tests.

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