DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism

doi:10.1186/s12880-023-01103-5

	DeepHipp: accurate segmentation of hippocampus using 3D dense-block based on attention mechanism
	Wang, Han 1; Lei, Cai 1; Zhao, Di 2; Gao, Liwei 3; Gao, Jingyang 1
	2023-10-13
发表期刊	BMC MEDICAL IMAGING
ISSN	1471-2342
卷号	23 期号:1 页码:15
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.
关键词	Segmentation of hippocampus Deep learning Dense block, Attention, Data augmentation
DOI	10.1186/s12880-023-01103-5
收录类别	SCI
语种	英语
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
WOS类目	Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001086693300002
出版者	BMC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/21106
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.