Deep Learning Based Visceral Adipose Tissue Segmentation Using Abdominal Images on MRI

Authors

  • Sudha Devi. B  Research Scholar, Department of Computer Science, Nesamony Memorial Christian College Marthandam, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
  • Dr. D. S. Misbha  Assistant Professor, Nesamony Memorial Christian College, Marthandam, Tamil Nadu, India

Keywords:

Visceral adipose tissue (VAT), Subcutaneous adipose tissue (SAT), Convolutional neural network, Deep learning.

Abstract

Obesity is closely related with increased risk of many diseases such as diabetes, hypertension, strokes, cardiovascular diseases and cancer. MRI is an accurate and most prevalent method to determine body fat distribution and quantification that causes obesity. The purpose of this work was to propose a novel approach for segmenting subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) using MR images of abdomen. A deep convolutional neural network was used to segment VAT and SAT. The segmentation results were compared to the ground truth produced by base-line CNN. The Dice coefficients, and accuracy shows similarity between two methods in quantifying VAT and SAT. This study demonstrates the feasibility of applying a new deep learning based scheme to automatically segment VAT from MRI data with high accuracy.

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International Journal of Scientific Research in Science and Technology

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Published

2021-04-10

Issue

Volume 9, Issue 1, March-April-2021

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Research Articles

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How to Cite

[1]
Sudha Devi. B, Dr. D. S. Misbha, " Deep Learning Based Visceral Adipose Tissue Segmentation Using Abdominal Images on MRI, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 9, Issue 1, pp.540-547, March-April-2021.