Femoral head segmentation based on improved fully convolutional neural network for ultrasound images

doi:10.1007/s11760-020-01637-z

	Femoral head segmentation based on improved fully convolutional neural network for ultrasound images
	Chen, Lei 1; Cui, Yutao 1; Song, Hong 1; Huang, Bingxuan 2; Yang, Jian 3; Zhao, Di 4; Xia, Bei 2
	2020-07-01
发表期刊	SIGNAL IMAGE AND VIDEO PROCESSING
ISSN	1863-1703
卷号	14 期号:5 页码:1043-1051
摘要	Developmental dysplasia of the hip is a medical term representing the hip joint instability that appears mainly in infants. The assessment metric of physician is based on the femoral head coverage rate, which needs to segment the femoral head area in 2D ultrasound images. In this paper, we propose an approach to automatically segment the femoral head. The proposed method consists of two parts, firstly, mean filtering, morphological processing and least squares operation are used to detect the ilium and acetabular bone baseline to coarsely obtain the region of interest of the femoral head, then followed by an improved fully convolutional neural network named FNet which integrates the convolution encoder-decoder architecture, pooling indices and residual connection operation for more accurate segmentation. FNet is trained in a cascaded way, which can help the network learn more features with a limited dataset and thus further improve the segmentation performance. Experimental results show that the proposed method achieved an average dice, recall and IoU value of 0.946, 0.937 and 0.897. Moreover, the features learned by convolutional layers are visualized to demonstrate that FNet can focus on significant features, which is helpful to restore the contour of the femoral head more precisely. In conclusion, the proposed method is capable of segmenting femoral head accurately and guiding the diagnosis of developmental dysplasia of the hip.
关键词	Developmental dysplasia of the hip Femoral head segmentation Fully convolutional neural networks Feature visualization
DOI	10.1007/s11760-020-01637-z
收录类别	SCI
语种	英语
WOS研究方向	Engineering ; Imaging Science & Photographic Technology
WOS类目	Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS记录号	WOS:000537827700022
出版者	SPRINGER LONDON LTD
引用统计	被引频次：4[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/2XEOYT63/15242
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Song, Hong
作者单位	1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China 2.Shenzhen Childrens Hosp, Shenzhen 518038, Peoples R China 3.Beijing Inst Technol, Sch Opt & Elect, Beijing 100081, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Chen, Lei,Cui, Yutao,Song, Hong,et al. Femoral head segmentation based on improved fully convolutional neural network for ultrasound images[J]. SIGNAL IMAGE AND VIDEO PROCESSING,2020,14(5):1043-1051.
APA	Chen, Lei.,Cui, Yutao.,Song, Hong.,Huang, Bingxuan.,Yang, Jian.,...&Xia, Bei.(2020).Femoral head segmentation based on improved fully convolutional neural network for ultrasound images.SIGNAL IMAGE AND VIDEO PROCESSING,14(5),1043-1051.
MLA	Chen, Lei,et al."Femoral head segmentation based on improved fully convolutional neural network for ultrasound images".SIGNAL IMAGE AND VIDEO PROCESSING 14.5(2020):1043-1051.