Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality - 3D Reconstruction and Segmentation Using 3D U-Net Architecture

Sep 24, 2026

Osteosarcoma is a prevalent bone tumor in children and adolescents (ages 5–25), where manual MRI tumor delineation is labor-intensive, subjective, and hard to reproduce. An automated convolutional neural network approach was developed to segment osteosarcoma on three MRI modalities (T1W, T2W, and T1W+Gd). From 3692 DICOM images of 46 patients, preprocessing included contrast stretching, median filtering, and reconstruction into NIfTI for deep learning. A 3D U-Net model produced mean Dice similarity coefficients of 83.75%, 85.45%, and 87.62% respectively, while showing limitations such as poorly defined borders and missing lesion portions.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

1. Introduction

Osteosarcoma background and clinical need

MRI imaging and segmentation challenges

Imaging modalities and planning relevance

Deep learning solution overview

3D U-Net segmentation approach

diagnostics Article Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture Chee Chin Lim 1,2, *, Apple Ho Wei Ling 1, Yen Fook Chong 2, Mohd Yusoff Mashor 1,2, Khalilalrahman Alshantti 3 and Mohd Ezane Aziz 3 Citation: Lim, C.C.; Ling, A.H.W.; Chong, Y.F.; Mashor, M.Y.; Alshantti, K.; Aziz, M.E. Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture. Diagnostics 2023, 13, 2377. [https://](https://)[ ](https://)[doi.org/10.3390/diagnostics13142377](doi.org/10.3390/diagnostics13142377) Academic Editor: Dario Marchetti Received: 31 May 2023 Revised: 29 June 2023 Accepted: 5 July 2023 Published: 14 July 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia; [appleling0504@gmail.com](appleling0504@gmail.com) (A.H.W.L.); [yusoff@unimap.edu.my](yusoff@unimap.edu.my) (M.Y.M.) 2 Sport Engineering Research Centre (SERC), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia; [fook1987@gmail.com](fook1987@gmail.com) 3 Department of Radiology, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia; [khalilalshantti@gmail.com](khalilalshantti@gmail.com) (K.A.); [drezane@usm.my](drezane@usm.my) (M.E.A.) * Correspondence: [cclim@unimap.edu.my or](cclim@unimap.edu.my or) [cheechin10@gmail.com](cheechin10@gmail.com); Tel.: +60-12-2369958 Abstract: Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results may be subjective and difﬁcult to replicate. Therefore, a convolutional neural network (CNN) was developed to automatically segment osteosarcoma cancerous cells in three types of MRI images. The study consisted of ﬁve main stages. First, 3692 DICOM format MRI images were acquired from 46 patients, including T1-weighted, T2-weighted, and T1-weighted with injection of Gadolinium (T1W + Gd) images. Contrast stretching and median ﬁlter were applied to enhance image intensity and remove noise, and the pre-processed images were reconstructed into NIfTI format ﬁles for deep learning. The MRI images were then transformed to ﬁt the CNN's requirements. A 3D U-Net architecture was proposed with optimized parameters to build an automatic segmentation model capable of segmenting osteosarcoma from the MRI images. The 3D U-Net segmentation model achieved excellent results, with mean dice similarity coefﬁcients (DSC) of 83.75%, 85.45%, and 87.62% for T1W, T2W, and T1W + Gd images, respectively. However, the study found that the proposed method had some limitations, including poorly deﬁned borders, missing lesion portions, and other confounding factors. In summary, an automatic segmentation method based on a CNN has been developed to address the challenge of manually segmenting osteosarcoma cancerous cells in MRI images. While the proposed method showed promise, the study revealed limitations that need to be addressed to improve its efﬁcacy. Keywords: osteosarcoma; bone cancerous cell; deep learning; convolutional neural network; 3DU-Net; MRI; tumor segmentation 1. Introduction Osteosarcoma is one of the most prevalent bone tumors, affecting mostly children and adolescents, typically those between 5 and 25 years of age who are experiencing puberty growth spurts [1] . Osteosarcoma usually develops from osteobl

FAQs

Why is automated osteosarcoma segmentation needed for MRI images?

Manual tumor delineation on MRI is time-consuming, subjective, and produces non-repeatable findings. Automated segmentation can improve consistency for clinical workflows.

What data and preprocessing steps were used to train the model?

The study acquired 3692 DICOM MRI images from 46 patients across T1W, T2W, and T1W+Gd. Contrast stretching and median filtering were applied, and images were reconstructed into NIfTI format for deep learning.

How effective was the proposed 3D U-Net segmentation model?

The model achieved mean Dice similarity coefficients of 83.75% (T1W), 85.45% (T2W), and 87.62% (T1W+Gd).

Sep 24, 2026

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