A Modified Parallel NET (MPNET)-Based Deep Learning Technique for the Segmentation of Visceral and Superficial Adipose Tissues Quantification of CT Scans

Adekanbi, Josteve and Das, Debashish and Elmitwally, Nouh and Ali, Aliyuda and Madai, Vince I. and Bahadar, Bhatia (2025) A Modified Parallel NET (MPNET)-Based Deep Learning Technique for the Segmentation of Visceral and Superficial Adipose Tissues Quantification of CT Scans. IEEE Access. ISSN 2169-3536

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Abstract

This study introduces a modified parallel net (MPNET), a novel deep learning model designed for accurate segmentation and quantification of visceral and superficial adipose tissues. This was used to quantify the visceral and superficial adipose tissues found at the L3 levels of vertebra in CT scans. This will be used to predict the likelihood of the patient developing diabetes or cardiovascular diseases from existing CT scan data. MPNET was compared with state-of-the-art models like UNET, R2UNET, UNET++, and nnUNET. This approach advances the accuracy and efficiency of image segmentation demonstrating a faster learning curve and lower losses at early epochs than traditional models., We developed and validated using a limited dataset of 14 single-slice DICOM files for each patient extracted from the National Health Service UK. The outputs from MPNET not only matched but often exceeded traditional metrics such as the Dice coefficient and IoU in nuanced anatomical delineation, providing greater clinical realism and applicability in segmentation results. As a pilot study, this research paves the way for a forthcoming validation study on a larger and more ethnically diverse dataset.

Item Type:	Article
Identification Number:	10.1109/ACCESS.2025.3538626
Dates:	Date Event 28 January 2025 Accepted 4 February 2025 Published Online
Uncontrolled Keywords:	Convolutional Neural Networks, CT Scan Analysis, Deep Learning, Image Post- Processing, Segmentation and Quantification, Visceral and Superficial Adipose Tissues
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Nouh Elmitwally
Date Deposited:	11 Feb 2025 10:29
Last Modified:	11 Feb 2025 10:29
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16132

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