Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network
Precisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer’s disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can significantly increase hippocampus segmentati...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7921 |
| _version_ | 1797592063777177600 |
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| author | Juan Jiang Hong Liu Xin Yu Jin Zhang Bing Xiong Lidan Kuang |
| author_facet | Juan Jiang Hong Liu Xin Yu Jin Zhang Bing Xiong Lidan Kuang |
| author_sort | Juan Jiang |
| collection | DOAJ |
| description | Precisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer’s disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can significantly increase hippocampus segmentation performance. First, a dynamic convolution block is designed to extract information more comprehensively in the steps of the 3D U-Net’s encoder and decoder. In addition, an improved coordinate attention algorithm is applied in the skip connections step of the 3D U-Net to increase the weight of the hippocampus and reduce the redundancy of other unimportant location information. The algorithm proposed in this work uses soft pooling methods instead of max pooling to reduce information loss during downsampling steps. The datasets employed in this research were obtained from the MICCAI 2013 SATA Challenge (MICCAI) and the Harmonized Protocol initiative of the Alzheimer’s Disease Neuroimaging Initiative (HarP). The experimental results on the two datasets prove that the algorithm proposed in this work outperforms other commonly used segmentation algorithms. On the HarP, the dice increase by 3.52%, the mIoU increases by 2.65%, and the F1 score increases by 3.38% in contrast to the baseline. On the MICCAI, the dice, the mIoU, and the F1 score increase by 1.13%, 0.85%, and 1.08%, respectively. Overall, the proposed model outperforms other common algorithms. |
| first_indexed | 2024-03-11T01:46:07Z |
| format | Article |
| id | doaj.art-650adc9d64194b2599cda50a6625e7f8 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:07Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-650adc9d64194b2599cda50a6625e7f82023-11-18T16:13:22ZengMDPI AGApplied Sciences2076-34172023-07-011313792110.3390/app13137921Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution NetworkJuan Jiang0Hong Liu1Xin Yu2Jin Zhang3Bing Xiong4Lidan Kuang5College of Information Science and Engineering, Hunan Normal University, Changsha 410081, ChinaCollege of Information Science and Engineering, Hunan Normal University, Changsha 410081, ChinaCollege of Information Science and Engineering, Hunan Normal University, Changsha 410081, ChinaCollege of Information Science and Engineering, Hunan Normal University, Changsha 410081, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSchool of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaPrecisely segmenting the hippocampus from the brain is crucial for diagnosing neurodegenerative illnesses such as Alzheimer’s disease, depression, etc. In this research, we propose an enhanced hippocampus segmentation algorithm based on 3D U-Net that can significantly increase hippocampus segmentation performance. First, a dynamic convolution block is designed to extract information more comprehensively in the steps of the 3D U-Net’s encoder and decoder. In addition, an improved coordinate attention algorithm is applied in the skip connections step of the 3D U-Net to increase the weight of the hippocampus and reduce the redundancy of other unimportant location information. The algorithm proposed in this work uses soft pooling methods instead of max pooling to reduce information loss during downsampling steps. The datasets employed in this research were obtained from the MICCAI 2013 SATA Challenge (MICCAI) and the Harmonized Protocol initiative of the Alzheimer’s Disease Neuroimaging Initiative (HarP). The experimental results on the two datasets prove that the algorithm proposed in this work outperforms other commonly used segmentation algorithms. On the HarP, the dice increase by 3.52%, the mIoU increases by 2.65%, and the F1 score increases by 3.38% in contrast to the baseline. On the MICCAI, the dice, the mIoU, and the F1 score increase by 1.13%, 0.85%, and 1.08%, respectively. Overall, the proposed model outperforms other common algorithms.https://www.mdpi.com/2076-3417/13/13/7921hippocampus segmentationdynamic convolutionattention mechanism3D U-Net |
| spellingShingle | Juan Jiang Hong Liu Xin Yu Jin Zhang Bing Xiong Lidan Kuang Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network Applied Sciences hippocampus segmentation dynamic convolution attention mechanism 3D U-Net |
| title | Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network |
| title_full | Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network |
| title_fullStr | Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network |
| title_full_unstemmed | Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network |
| title_short | Hippocampus Segmentation Method Applying Coordinate Attention Mechanism and Dynamic Convolution Network |
| title_sort | hippocampus segmentation method applying coordinate attention mechanism and dynamic convolution network |
| topic | hippocampus segmentation dynamic convolution attention mechanism 3D U-Net |
| url | https://www.mdpi.com/2076-3417/13/13/7921 |
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