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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.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/comparative-analysis-of-image-processing-techniques-for-enhanced-mri-image-quality-3d-reconstruction-and-segmentation-using-3d-u-net-architecture/386108/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/comparative-analysis-of-image-processing-techniques-for-enhanced-mri-image-quality-3d-reconstruction-and-segmentation-using-3d-u-net-architecture/386108.png","ImageObject",300,407,{"name":92,"@type":93},"Riley West","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-25","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why is automated osteosarcoma segmentation needed for MRI images?","Question",{"text":112,"@type":113},"Manual tumor delineation on MRI is time-consuming, subjective, and produces non-repeatable findings. Automated segmentation can improve consistency for clinical workflows.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and preprocessing steps were used to train the model?",{"text":117,"@type":113},"The study acquired 3692 DICOM MRI images from 46 patients across T1W, T2W, and T1W+Gd. 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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)  \nAcademic Editor: Dario Marchetti  \nReceived: 31 May 2023  \nRevised: 29 June 2023  \nAccepted: 5 July 2023  \nPublished: 14 July 2023  \nCopyright: © 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/)) .  \n1 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.)  \n2 Sport Engineering Research Centre (SERC), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia; [fook1987@gmail.com](fook1987@gmail.com)  \n3 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.)  \n* Correspondence: [cclim@unimap.edu.my or](cclim@unimap.edu.my or) [cheechin10@gmail.com](cheechin10@gmail.com); Tel.: +60-12-2369958  \nAbstract: 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.  \nKeywords: osteosarcoma; bone cancerous cell; deep learning; convolutional neural network; 3DU-Net; MRI; tumor segmentation  \n1. Introduction  \nOsteosarcoma 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","cbCain3WFwVKlHc7","https://ap.wps.com/l/cbCain3WFwVKlHc7","pdf",13867194,25,"English","# 1. Introduction\n## Osteosarcoma background and clinical need\n# MRI imaging and segmentation challenges\n## Imaging modalities and planning relevance\n# Deep learning solution overview\n## 3D U-Net segmentation approach","[{\"question\":\"Why is automated osteosarcoma segmentation needed for MRI images?\",\"answer\":\"Manual tumor delineation on MRI is time-consuming, subjective, and produces non-repeatable findings. Automated segmentation can improve consistency for clinical workflows.\"},{\"question\":\"What data and preprocessing steps were used to train the model?\",\"answer\":\"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.\"},{\"question\":\"How effective was the proposed 3D U-Net segmentation model?\",\"answer\":\"The model achieved mean Dice similarity coefficients of 83.75% (T1W), 85.45% (T2W), and 87.62% (T1W+Gd).\"}]","Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality - 3D Reconstruction and Segmentation Using 3D U-Net Architecture | PDF",1790272286,63]