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An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography.
Guo, Ziyu; Zhang, Yuting; Qiu, Zishan; Dong, Suyu; He, Shan; Gao, Huan; Zhang, Jinao; Chen, Yingtao; He, Bingtao; Kong, Zhe; Qiu, Zhaowen; Li, Yan; Li, Caijuan.
Afiliación
  • Guo Z; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • Zhang Y; School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
  • Qiu Z; College of Art and Science, New York University Shanghai, Shanghai, China.
  • Dong S; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • He S; School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
  • Gao H; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • Zhang J; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • Chen Y; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • He B; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • Kong Z; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • Qiu Z; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • Li Y; College of Computer and Control Engineering, Northeast Forestry University, Harbin, China.
  • Li C; Department of Medical Ultrasonics, Hongqi Hospital of Mudanjiang Medical University, Mudanjiang, China.
Front Cardiovasc Med ; 10: 1266260, 2023.
Article en En | MEDLINE | ID: mdl-37808878
Cardiac diseases have high mortality rates and are a significant threat to human health. Echocardiography is a commonly used imaging technique to diagnose cardiac diseases because of its portability, non-invasiveness and low cost. Precise segmentation of basic cardiac structures is crucial for cardiologists to efficiently diagnose cardiac diseases, but this task is challenging due to several reasons, such as: (1) low image contrast, (2) incomplete structures of cardiac, and (3) unclear border between the ventricle and the atrium in some echocardiographic images. In this paper, we applied contrastive learning strategy and proposed a semi-supervised method for echocardiographic images segmentation. This proposed method solved the above challenges effectively and made use of unlabeled data to achieve a great performance, which could help doctors improve the accuracy of CVD diagnosis and screening. We evaluated this method on a public dataset (CAMUS), achieving mean Dice Similarity Coefficient (DSC) of 0.898, 0.911, 0.916 with 1/4, 1/2 and full labeled data on two-chamber (2CH) echocardiography images, and of 0.903, 0.921, 0.928 with 1/4, 1/2 and full labeled data on four-chamber (4CH) echocardiography images. Compared with other existing methods, the proposed method had fewer parameters and better performance. The code and models are available at https://github.com/gpgzy/CL-Cardiac-segmentation.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Cardiovasc Med Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Cardiovasc Med Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza