Automatic Segmentation Using U-Net for Accurate Liver Segmentation of CT Images

Authors

  • S. V. Vanmore Electronics Engineering, Department of Technology, Shivaji University, Kolhapur, 416004, Maharashtra, India
  • S. S. Nikam AISSMS Institute of Information Technology, Pune, India.
  • R. B. Dhumale AISSMS Institute of Information Technology, Pune, India.
  • N. R. Dhumale Sinhgad College of Engineering, Pune, India.
  • P. B. Mane AISSMS Institute of Information Technology, Pune, India.
  • A. N. Sarwade Sinhgad College of Engineering, Pune, India.

Keywords:

Liver CT images, Segmentation, Deep neural network, U-Net, Convolution Neural Network

Abstract

In this paper a high-resolution U-Net architecture for the segmentation of liver Computed Tomography (CT) scan images with high accuracy is presented. The contraction and expansion paths present in the proposed U-Net allow for reducing computational time and increase the resolution of information of the liver CT scan image respectively. The high-resolution U-Net architecture has been trained and validated using an IRCAD 2D liver CT image dataset. Performance of the U-Net architecture has been verified by calculating performance metrics of segmented 2D liver CT scan images. Results of performance metrics show that the proposed U-Net architecture achieves the best quality performance. The maximum dice coefficient of the liver segmentation during training phase is 95.79 % whereas during validation phase is 89.27 %. The high-resolution U-Net architecture has also compared with other researcher’s work in this paper.

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Published

21.09.2023

How to Cite

Vanmore, S. V. ., Nikam, S. S. ., Dhumale, R. B. ., Dhumale, N. R. ., Mane, P. B. ., & Sarwade, A. N. . (2023). Automatic Segmentation Using U-Net for Accurate Liver Segmentation of CT Images. International Journal of Intelligent Systems and Applications in Engineering, 11(4), 446–452. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3542

Issue

Vol. 11 No. 4 (2023)

Section

Research Article

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