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Robust segmentation of mass‐lesions in contrast‐enhanced dynamic breast MR images
Author(s) -
Meinel Lina A.,
Buelow Thomas,
Huo Dezheng,
Shimauchi Akiko,
Kose Ursula,
Buurman Johannes,
Newstead Gillian
Publication year - 2010
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.22206
Subject(s) - segmentation , contouring , computer science , artificial intelligence , region of interest , reproducibility , breast mri , region growing , pattern recognition (psychology) , similarity (geometry) , image segmentation , computer vision , nuclear medicine , scale space segmentation , medicine , mathematics , mammography , breast cancer , image (mathematics) , statistics , computer graphics (images) , cancer
Purpose: To develop and evaluate a computerized segmentation method for breast MRI (BMRI) mass‐lesions. Materials and Methods: A computerized segmentation algorithm was developed to segment mass‐like‐lesions on breast MRI. The segmentation algorithm involved: (i) interactive lesion selection, (ii) automatic intensity threshold estimation, (iii) connected component analysis, and (iv) a postprocessing procedure for hole‐filling and leakage removal. Seven observers manually traced the borders of all slices of 30 mass‐lesions using the same tools. To initiate the computerized segmentation, each user selected a seed‐point for each lesion interactively using two methods: direct seed‐point and robust region of interest (ROI) selections. The manual and computerized segmentations were compared pair‐wise using the measured size and overlap to evaluate similarity, and the reproducibility of the computerized segmentation was compared with the interobserver variability of the manual delineations. Results: The observed inter‐ and intraobserver variations were similar ( P > 0.05). Computerized segmentation using the robust ROI selection method was significantly ( P < 0.001) more reproducible in measuring lesion size (stDev 1.8%) than either manual contouring (11.7%) or computerized segmentation using directly placed seed‐point method (13.7%). Conclusion: The computerized segmentation method using robust ROI selection is more reproducible than manual delineation in terms of measuring the size of a mass‐lesion. J. Magn. Reson. Imaging 2010;32:110–119. © 2010 Wiley‐Liss, Inc.