Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks
This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmenta...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Stem Cells International |
| Online Access: | http://dx.doi.org/10.1155/2022/8619690 |
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| _version_ | 1832565408438157312 |
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| author | Jie Dong Yueying Zhang Yun Meng Tingxiao Yang Wei Ma Huixin Wu |
| author_facet | Jie Dong Yueying Zhang Yun Meng Tingxiao Yang Wei Ma Huixin Wu |
| author_sort | Jie Dong |
| collection | DOAJ |
| description | This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. The results showed that the CGSSNet network segmentation algorithm significantly improved the segmentation accuracy of glioma MRI images. In addition, its DSC, sensitivity, and HD values for glioma MRI images were 0.937, 0.811, and 1.201, respectively. Under different iteration times, the DSC, sensitivity, and HD values of the CGSSNet network segmentation algorithm are significantly better than other algorithms. It showed that the CGSSNet model based on the DenseNet can improve the segmentation accuracy of glioma MRI images, and has potential application value in clinical practice. |
| format | Article |
| id | doaj-art-d881cc57e07942ddb4c41738b27035ec |
| institution | Kabale University |
| issn | 1687-9678 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stem Cells International |
| spelling | doaj-art-d881cc57e07942ddb4c41738b27035ec2025-02-03T01:07:56ZengWileyStem Cells International1687-96782022-01-01202210.1155/2022/8619690Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional NetworksJie Dong0Yueying Zhang1Yun Meng2Tingxiao Yang3Wei Ma4Huixin Wu5School of Information EngineeringSchool of Information EngineeringDepartment of Magnetic ResonanceSchool of Information EngineeringSchool of Information EngineeringSchool of Information EngineeringThis work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. The results showed that the CGSSNet network segmentation algorithm significantly improved the segmentation accuracy of glioma MRI images. In addition, its DSC, sensitivity, and HD values for glioma MRI images were 0.937, 0.811, and 1.201, respectively. Under different iteration times, the DSC, sensitivity, and HD values of the CGSSNet network segmentation algorithm are significantly better than other algorithms. It showed that the CGSSNet model based on the DenseNet can improve the segmentation accuracy of glioma MRI images, and has potential application value in clinical practice.http://dx.doi.org/10.1155/2022/8619690 |
| spellingShingle | Jie Dong Yueying Zhang Yun Meng Tingxiao Yang Wei Ma Huixin Wu Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks Stem Cells International |
| title | Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks |
| title_full | Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks |
| title_fullStr | Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks |
| title_full_unstemmed | Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks |
| title_short | Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks |
| title_sort | segmentation algorithm of magnetic resonance imaging glioma under fully convolutional densely connected convolutional networks |
| url | http://dx.doi.org/10.1155/2022/8619690 |
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