AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.
TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 1; pp. 74 - 84 |
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| Autores principales: | , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184471476&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184471476 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Feb2025 vid: 38 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184471476 184471476 184471476 10.1007/s10278-024-01187-7 184471476 ppf: 74 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis. aug: au: Lee, Myungeun Kim, Jong Hyo Choi, Wookjin Lee, Ki Hong affil: https://ror.org/05kzjxq56 Research Institute of Medical Sciences, Chonnam National University, Gwangju, Republic of Korea sug: subj: Artificial Intelligence Image Processing, Computer Assisted Brain Neoplasms Radiography Brain Neoplasms Pathology Magnetic Resonance Imaging Human Funding Source Male Female Glioma Radiography Glioma Pathology Imaging, Three-Dimensional Software Quantitative Studies Automation Brain Physiopathology Descriptive Statistics Comparative Studies Male Female ab: TumorPrism3D software was developed to segment brain tumors with a straightforward and user-friendly graphical interface applied to two- and three-dimensional brain magnetic resonance (MR) images. The MR images of 185 patients (103 males, 82 females) with glioblastoma multiforme were downloaded from The Cancer Imaging Archive (TCIA) to test the tumor segmentation performance of this software. Regions of interest (ROIs) corresponding to contrast-enhancing lesions, necrotic portions, and non-enhancing T2 high signal intensity components were segmented for each tumor. TumorPrism3D demonstrated high accuracy in segmenting all three tumor components in cases of glioblastoma multiforme. They achieved a better Dice similarity coefficient (DSC) ranging from 0.83 to 0.91 than 3DSlicer with a DSC ranging from 0.80 to 0.84 for the accuracy of segmented tumors. Comparative analysis with the widely used 3DSlicer software revealed TumorPrism3D to be approximately 37.4% faster in the segmentation process from initial contour drawing to final segmentation mask determination. The semi-automated nature of TumorPrism3D facilitates reproducible tumor segmentation at a rapid pace, offering the potential for quantitative analysis of tumor characteristics and artificial intelligence-assisted segmentation in brain MR imaging. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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