Robust segmentation of mass-lesions in contrast-enhanced dynamic breast MR images
- PMID: 20578017
- DOI: 10.1002/jmri.22206
Robust segmentation of mass-lesions in contrast-enhanced dynamic breast MR images
Abstract
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.
(c) 2010 Wiley-Liss, Inc.
Similar articles
-
A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images.Acad Radiol. 2006 Jan;13(1):63-72. doi: 10.1016/j.acra.2005.08.035. Acad Radiol. 2006. PMID: 16399033
-
Evaluation of the three-time-point method for diagnosis of breast lesions in contrast-enhanced MR mammography.Clin Imaging. 2006 May-Jun;30(3):160-5. doi: 10.1016/j.clinimag.2005.11.005. Clin Imaging. 2006. PMID: 16632149
-
Enhanced mass on contrast-enhanced breast MR imaging: Lesion characterization using combination of dynamic contrast-enhanced and diffusion-weighted MR images.J Magn Reson Imaging. 2008 Nov;28(5):1157-65. doi: 10.1002/jmri.21570. J Magn Reson Imaging. 2008. PMID: 18972357
-
Short first-pass MRI of the breast.Acta Radiol. 2012 Apr 1;53(3):267-9. doi: 10.1258/ar.2012.110638. Epub 2012 Feb 14. Acta Radiol. 2012. PMID: 22334872 Review.
-
On computerized methods for spine analysis in MRI: a systematic review.Int J Comput Assist Radiol Surg. 2016 Aug;11(8):1445-65. doi: 10.1007/s11548-016-1350-2. Epub 2016 Feb 9. Int J Comput Assist Radiol Surg. 2016. PMID: 26861655 Review.
Cited by
-
AI-Enhanced Diagnosis of Challenging Lesions in Breast MRI: A Methodology and Application Primer.J Magn Reson Imaging. 2021 Sep;54(3):686-702. doi: 10.1002/jmri.27332. Epub 2020 Aug 30. J Magn Reson Imaging. 2021. PMID: 32864782 Free PMC article. Review.
-
Creation of RTOG compliant patient CT-atlases for automated atlas based contouring of local regional breast and high-risk prostate cancers.Radiat Oncol. 2013 Jul 25;8:188. doi: 10.1186/1748-717X-8-188. Radiat Oncol. 2013. PMID: 23885662 Free PMC article.
-
Computerized segmentation and characterization of breast lesions in dynamic contrast-enhanced MR images using fuzzy c-means clustering and snake algorithm.Comput Math Methods Med. 2012;2012:634907. doi: 10.1155/2012/634907. Epub 2012 Aug 21. Comput Math Methods Med. 2012. PMID: 22952558 Free PMC article.
-
Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients.PLoS One. 2018 Jan 10;13(1):e0190348. doi: 10.1371/journal.pone.0190348. eCollection 2018. PLoS One. 2018. PMID: 29320532 Free PMC article.
Publication types
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Medical
