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

Mehreen Irshad, Mussarat Yasmin, Muhammad Imran Sharif, Muhammad Rashid, Muhammad Irfan Sharif and Seifedine Kadry ()
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Mehreen Irshad: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
Mussarat Yasmin: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
Muhammad Imran Sharif: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
Muhammad Rashid: Department of Computer Science, University of Turin, 10124 Turin, Italy
Muhammad Irfan Sharif: Department of Information Sciences, University of Education Lahore, Jauharabad Campus, Jauharabad 41200, Pakistan
Seifedine Kadry: Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway

Mathematics, 2023, vol. 11, issue 14, 1-21

Abstract: 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%.

Keywords: left ventricular segmentation; MRI; deep learning; image enhancement technique; histogram (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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