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.

Symmetric Biomechanically Guided Prone-to-Supine Breast Image Registration.

Prone-to-supine breast image registration has potential application in the fields of surgical and radiotherapy planning, image guided interventions, and multi-modal cancer diagnosis, staging, and therapy response prediction. However, breast image registration of three dimensional images acquired in different patient positions is a challenging problem, due to large deformations induced to the soft breast tissue caused by the change in gravity loading. We present a symmetric, biomechanical simulation based registration framework which aligns the images in a central, virtually unloaded configuration. The breast tissue is modelled as a neo-Hookean material and gravity is considered as the main source of deformation in the original images. In addition to gravity, our framework successively applies image derived forces directly into the unloading simulation in place of a subsequent image registration step. This results in a biomechanically constrained deformation. Using a finite difference scheme avoids an explicit meshing step and enables simulations to be performed directly in the image space. The explicit time integration scheme allows the motion at the interface between chest and breast to be constrained along the chest wall. The feasibility and accuracy of the approach presented here was assessed by measuring the target registration error (TRE) using a numerical phantom with known ground truth deformations, nine clinical prone MRI and supine CT image pairs, one clinical prone-supine CT image pair and four prone-supine MRI image pairs. The registration reduced the mean TRE for the numerical phantom experiment from initially 19.3 to 0.9 mm and the combined mean TRE for all fourteen clinical data sets from 69.7 to 5.6 mm.

Characterization of Cystic Lesions by Spectral Mammography: Results of a Clinical Pilot Study.

OBJECTIVES: Round lesions are a common mammographic finding, which can contribute more than 20% of overall recalls at screening. Discrimination of cystic fluid from solid tissue by spectral x-ray imaging has been demonstrated in specimen experiments. This work translates these results into a clinical pilot study to investigate the feasibility of discriminating cystic from solid lesions using spectral mammography. MATERIALS AND METHODS: Women undergoing mammography as part of their routine diagnostic workup were consented for analysis of spectral information obtained from a photon-counting mammography system. Images were analyzed retrospectively after diagnosis was confirmed with ultrasound and pathology. Well-defined solitary lesions were delineated independently by 3 expert radiologists. A breast lesion model is generated from the spectral mammography data using the energy-dependent x-ray attenuation of cyst fluid, carcinoma, and adipose and glandular tissue. From the breast lesion model, 2 spectral features are computed and combined in a 2-feature discrimination algorithm, which is evaluated in an analysis of the receiver operating characteristic curve for the task of identifying solid lesions ("positive result"). Expected outcomes on a screening population are extrapolated from this pilot study by cross-validation with bootstrapping using a 95% confidence interval (CI). RESULTS: The 2-feature discrimination algorithm was evaluated on the set of 119 eligible lesions (62 solids, 57 cysts) of diameter greater than 10 mm. The area under the receiver operating characteristic curve (AUC) was 0.88 with a specificity of 61% at the 99% sensitivity level on average over all expert radiologists. Cross-validation with bootstrapping of the clinical data revealed an AUC of 0.89 (95% CI, 0.79-0.96) and a specificity of 56% (95% CI, 33%-78%) when operating the algorithm at the 99% sensitivity level. CONCLUSIONS: Discriminating cystic from solid lesions with spectral mammography demonstrates promising results with the potential to reduce mammographic recalls. It is estimated that for each missed cancer at least 625 cystic lesions would have been correctly identified and hence would not have been needed to be recalled. Our results justify undertaking a larger reader study to refine the algorithm and determine clinically relevant thresholds to allow safe classification of cystic lesions by spectral mammography.