Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c

The goal of this study was to develop an automated method to segment breast masses on dynamic contrast-enhanced DCE magnetic resonance MR scans and to evaluate its potential for estimating tumor volume on preand postchemotherapy images and tumor change in response to treatment. A radiologist experienced in interpreting breast MR scans defined a cuboid volume of interest VOI enclosing the mass in the MR volume at one time point within the sequence of DCE-MR scans. The corresponding VOIs over the entire time sequence were then automatically extracted. A new 3D VOI representing the local pharmacokinetic activities in the VOI was generated from the 4D VOI sequence by summarizing the temporal intensity enhancement curve of each voxel with its standard deviation. The method then used the fuzzy c-means FCM clustering algorithm followed by morphological filtering for initial mass segmentation. The initial segmentation was refined by the 3D level set LS method. The velocity field of the LS method was formulated in terms of the mean curvature which guaranteed the smoothness of the surface, the Sobel edge information which attracted the zero LS to the desired mass margin, and the FCM membership function which improved segmentation accuracy. The method was evaluated on 50 DCE-MR scans of 25 patients who underwent neoadjuvant chemotherapy. Each patient had preand postchemotherapy DCE-MR scans on a 1.5 T magnet. The in-plane pixel size ranged from 0.546 to 0.703 mm and the slice thickness ranged from 2.5 to 4.5 mm. The flip angle was 15°, repetition time ranged from 5.98 to 6.7 ms, and echo time ranged from 1.2 to 1.3 ms. Computer segmentation was applied to the coronal T1weighted images. For comparison, the same radiologist who marked the VOI also manually segmented the mass on each slice. The performance of the automated method was quantified using an overlap measure, defined as the ratio of the intersection of the computer and the manual segmentation volumes to the manual segmentation volume. Preand postchemotherapy masses had overlap measures of 0.81 0.13 mean s.d. and 0.71 0.22, respectively. The percentage volume reduction PVR estimated by computer and the radiologist were 55.5 43.0% mean s.d. and 57.8 51.3%, respectively. Paired Student’s t test indicated that the difference between the mean PVRs estimated by computer and the radiologist did not reach statistical significance p=0.641 . The automated mass segmentation method may have the potential to assist physicians in monitoring volume change in breast masses in response to treatment. © 2009 American Association of Physicists in Medicine. DOI: 10.1118/1.3238101