A Novel Light U-Net Model for Left Ventricle Segmentation Using MRI

MRI segmentation and analysis are significant tasks in clinical cardiac computations. A cardiovascular MR scan with left ventricular segmentation seems necessary to diagnose and further treat the disease. The proposed method for left ventricle segmentation works as a combination of the intelligent h...

Full description

Saved in:
Bibliographic Details
Published inMathematics (Basel) Vol. 11; no. 14; p. 3245
Main Authors Irshad, Mehreen, Yasmin, Mussarat, Sharif, Muhammad Imran, Rashid, Muhammad, Sharif, Muhammad Irfan, Kadry, Seifedine
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Accuracy
Algorithms
Artificial intelligence
Automation
Coders
Datasets
Deep learning
Food science
Heart
histogram
Image contrast
Image enhancement
image enhancement technique
Image segmentation
left ventricular segmentation
Magnetic resonance imaging
Medical imaging
MRI
Neural networks
Semantics
Online AccessGet full text

Cover

More Information
Summary:MRI segmentation and analysis are significant tasks in clinical cardiac computations. A cardiovascular MR scan with left ventricular segmentation seems necessary to diagnose and further treat the disease. The proposed method for left ventricle segmentation works as a combination of the intelligent histogram-based image enhancement technique with a Light U-Net model. This technique serves as the basis for choosing the low-contrast image subjected to the stretching technique and produces sharp object contours with good contrast settings for the segmentation process. After enhancement, the images are subjected to the encoder–decoder configuration of U-Net using a novel lightweight processing model. Encoder sampling is supported by a block of three parallel convolutional layers with supporting functions that improve the semantics for segmentation at various levels of resolutions and features. The proposed method finally increased segmentation efficiency, extracting the most relevant image resources from depth-to-depth convolutions, filtering them through each network block, and producing more precise resource maps. The dataset of MICCAI 2009 served as an assessment tool of the proposed methodology and provides a dice coefficient value of 97.7%, accuracy of 92%, and precision of 98.17%.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11143245