Ciyamala Kushbu Sadhanandan, Inbamalar Tharcis Mariapushpam, Sudha Suresh

An efficient deep learning algorithm for the segmentation of cardiac ventricles

  • Electrical and Electronic Engineering
  • Computer Vision and Pattern Recognition
  • Software
  • Electronic, Optical and Magnetic Materials

AbstractFor the effective diagnosis of cardio vascular disease (CVD), anatomical characteristics of the heart must be examined, which depends on segmenting the cardiac tissues of interest and then classifying them into appropriate pathological groups. In recent years, deep learning (DL)‐based computer aided design (CAD) segmentation has been employed to automate the segmentation process. Despite the evolution of several DL methods, they still fail due to the shape variation of the heart in patients and the availability of a limited amount of data. This paper proposes an effective Saliency and Active Contour‐based Attention UNet3+ algorithm to segment the ventricles of the heart, which is a challenging task for most researchers, especially with an irregularly shaped right ventricle (RV) that varies over cardiac phases. The algorithm outperforms other state‐of‐the‐art methods in DC metrics, which proves its efficiency in automating the segmentation process.

Need a simple solution for managing your BibTeX entries? Explore CiteDrive!

  • Web-based, modern reference management
  • Collaborate and share with fellow researchers
  • Integration with Overleaf
  • Comprehensive BibTeX/BibLaTeX support
  • Save articles and websites directly from your browser
  • Search for new articles from a database of tens of millions of references
Try out CiteDrive

More from our Archive