AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis

被引:0
作者
Lee, Myungeun [1 ,2 ]
Kim, Jong Hyo [3 ,4 ]
Choi, Wookjin [5 ]
Lee, Ki Hong [2 ,6 ]
机构
[1] Chonnam Natl Univ, Res Inst Med Sci, Gwangju, South Korea
[2] Chonnam Natl Univ Hosp, Dept Cardiovasc Med, Gwangju, South Korea
[3] Seoul Natl Univ Hosp, Dept Radiol, Seoul, South Korea
[4] Seoul Natl Univ, Grad Sch Convergence Sci & Technol, Dept Appl Bioengn, Suwon, South Korea
[5] Thomas Jefferson Univ, Sidney Kimmel Med Coll, Dept Radiat Oncol, Philadelphia, PA USA
[6] Chonnam Natl Univ, Med Sch, Dept Internal Med, Gwangju, South Korea
来源
JOURNAL OF IMAGING INFORMATICS IN MEDICINE | 2025年 / 38卷 / 01期
基金
新加坡国家研究基金会;
关键词
Segmentation; Semi-Automated; Magnetic Resonance Imaging; Brain Tumor;
D O I
10.1007/s10278-024-01187-7
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
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.
引用
收藏
页码:74 / 83
页数:10
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