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.
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.
Authors: Vikram M Velker; George B Rodrigues; Robert Dinniwell; Jeremiah Hwee; Alexander V Louie Journal: Radiat Oncol Date: 2013-07-25 Impact factor: 3.481