Deep Learning-Based Segmentation of Cardiac Structures in MRI Image

Authors

  • Kapil Rajput Asst. Professor, Department of Comp. Sc. & Info. Tech. Graphic Era Hill University, Dehradun Uttarakhand 248002
  • Aditya Agnihotri Lecturer, Department of Comp. Sc. & Info. Tech. Graphic Era Hill University, Dehradun Uttarakhand 248002
  • Ajay Hindurao Deshmukh Assistant Professor Department of Psychiatry Krishna Institute of Medical Sciences, Krishna Vishwa Vidyapeeth “Deemed To Be University” Karad Malkapur, Karad (Dist. Satara), Maharashtra, India. PIN – 415539

Keywords:

cardiac imagine segmentation, deep learning datasets

Abstract

Deep learning is now the most common technique for segmenting cardiac images, having recently eclipsed all previous approaches. In this research, we demonstrate a complete application of deep learning for cardiac image segmentation. This usage covers a broad variety of popular imaging modalities as well as the primary anatomical components that are significant (ventricles, atria, and arteries). In addition, we provide an overview of open-source software repositories as well as cardiac imaging datasets in order to encourage research that can be reproduced. Finally, we emphasize the limitations and restrictions of current deep learning-based approaches (a lack of labels, domain-general designs, and a lack of interpretability), and we offer future research pathways to address these issues.

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Published

01.07.2023

How to Cite

Rajput, K. ., Agnihotri, A. ., & Deshmukh , A. H. . (2023). Deep Learning-Based Segmentation of Cardiac Structures in MRI Image. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 72–78. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2932

Issue

Vol. 11 No. 7s (2023): Advancements in Machine Learning for Computer Science and Decision Support Systems

Section

Research Article

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