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Zhu K, Fu Z, Tao P, et al. [Left ventricle segmentation in echocardiography based on adaptive mean shift]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018;35(2):273-279doi: 10.7507/1001-5515.201702037.
Zhu, K., Fu, Z., Tao, P., & Zhu, S. (2018). [Left ventricle segmentation in echocardiography based on adaptive mean shift]. Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 35(2), 273-279. https://doi.org/10.7507/1001-5515.201702037
Zhu, Kai, et al. "[Left ventricle segmentation in echocardiography based on adaptive mean shift]." Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi vol. 35,2 (2018): 273-279. doi: https://doi.org/10.7507/1001-5515.201702037
Zhu K, Fu Z, Tao P, Zhu S. [Left ventricle segmentation in echocardiography based on adaptive mean shift]. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Apr 25;35(2):273-279. doi: 10.7507/1001-5515.201702037. Chinese. PMID: 29745534.
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Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2018 Apr 25;35(2):273-279. doi: 10.7507/1001-5515.201702037.

[Left ventricle segmentation in echocardiography based on adaptive mean shift].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

[Article in Chinese]
Kai Zhu, Zhongliang Fu, Pan Tao, Shuo Zhu

Affiliations

  1. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China;University of Chinese Academy of Sciences, Beijing 100049, [email protected].
  2. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China.
  3. Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, P.R.China;University of Chinese Academy of Sciences, Beijing 100049, P.R.China.
  4. Guizhou Medcial University, Guiyang 550025, P.R.China.

PMID: 29745534 DOI: 10.7507/1001-5515.201702037

Abstract

The use of echocardiography ventricle segmentation can obtain ventricular volume parameters, and it is helpful to evaluate cardiac function. However, the ultrasound images have the characteristics of high noise and difficulty in segmentation, bringing huge workload to segment the object region manually. Meanwhile, the automatic segmentation technology cannot guarantee the segmentation accuracy. In order to solve this problem, a novel algorithm framework is proposed to segment the ventricle. Firstly, faster region-based convolutional neural network is used to locate the object to get the region of interest. Secondly,

Keywords: adaptive bandwidth; left ventricle localization; left ventricle segmentation; mean shift segmentation; pixel clustering

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