DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism
文献类型：期刊论文
| 作者 | Wang, Han1; Lei, Cai1; Zhao, Di2; Gao, Liwei3; Gao, Jingyang1 |
| 刊名 | BMC MEDICAL IMAGING
 |
| 出版日期 | 2023-10-13 |
| 卷号 | 23期号:1页码:15 |
| ISSN号 | 1471-2342 |
| 关键词 | Segmentation of hippocampus Deep learning Dense block, Attention, Data augmentation |
| DOI | 10.1186/s12880-023-01103-5 |
| 英文摘要 | Background The hippocampus is a key area of the brain responsible for learning, memory, and other abilities. Accurately segmenting the hippocampus and precisely calculating the volume of the hippocampus is of great significance for predicting Alzheimer's disease and amnesia. Most of the segmentation algorithms currently involved are based on templates, such as the more popular FreeSufer.Methods This study proposes Deephipp, a deep learning network based on a 3D dense block using an attention mechanism for accurate segmentation of the hippocampus. DeepHipp is based on the following novelties: (i) DeepHipp adopts powerful data augmentation schemes to enhance the segmentation ability. (ii) DeepHipp is designed to incorporate 3D dense-block to capture multiple-scale features of the hippocampus. (iii) DeepHipp creatively uses the attention mechanism in the field of hippocampal image segmentation, extracting useful hippocampus information in a massive feature map, and improving the accuracy and sensitivity of the model.Conclusions We describe the illustrative results and show extensive qualitative and quantitative comparisons with other methods. Our achievement demonstrates that the accuracy of DeepHipp can reach 83.63%, which is superior to most existing methods in terms of accuracy and efficiency of hippocampus segmentation. It is noticeable that deep learning can potentially lead to an effective segmentation of medical images. |
| 资助项目 | We would like to thank Qiang Gao, Zezhong Zhang for useful discussions. |
| WOS研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
| 语种 | 英语 |
| 出版者 | BMC |
| WOS记录号 | WOS:001086693300002 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/21105]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Zhao, Di; Gao, Liwei; Gao, Jingyang |
| 作者单位 | 1.Beijing Univ Chem Technol, Dept Informat Sci & Technol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 3.China Japan Friendship Hosp, Dept Radiat Oncol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Han,Lei, Cai,Zhao, Di,et al. DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism[J]. BMC MEDICAL IMAGING,2023,23(1):15. |
| APA | Wang, Han,Lei, Cai,Zhao, Di,Gao, Liwei,&Gao, Jingyang.(2023).DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism.BMC MEDICAL IMAGING,23(1),15. |
| MLA | Wang, Han,et al."DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism".BMC MEDICAL IMAGING 23.1(2023):15. |
入库方式： OAI收割
来源：计算技术研究所
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