Robust segmentation of mass-lesions in contrast-enhanced dynamic breast MR images.

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.