Comb-push ultrasound shear elastography of breast masses: initial results show promise.

To evaluate the performance of Comb-push Ultrasound Shear Elastography (CUSE) for classification of breast masses.CUSE is an ultrasound-based quantitative two-dimensional shear wave elasticity imaging technique, which utilizes multiple laterally distributed acoustic radiation force (ARF) beams to simultaneously excite the tissue and induce shear waves. Female patients who were categorized as having suspicious breast masses underwent CUSE evaluations prior to biopsy. An elasticity estimate within the breast mass was obtained from the CUSE shear wave speed map. Elasticity estimates of various types of benign and malignant masses were compared with biopsy results.Fifty-four female patients with suspicious breast masses from our ongoing study are presented. Our cohort included 31 malignant and 23 benign breast masses. Our results indicate that the mean shear wave speed was significantly higher in malignant masses (6 ± 1.58 m/s) in comparison to benign masses (3.65 ± 1.36 m/s). Therefore, the stiffness of the mass quantified by the Young's modulus is significantly higher in malignant masses. According to the receiver operating characteristic curve (ROC), the optimal cut-off value of 83 kPa yields 87.10% sensitivity, 82.61% specificity, and 0.88 for the area under the curve (AUC).CUSE has the potential for clinical utility as a quantitative diagnostic imaging tool adjunct to B-mode ultrasound for differentiation of malignant and benign breast masses

Automated and real-time segmentation of suspicious breast masses using convolutional neural network

<div><p>In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy. The computer-aided detection tool effectively segmented the breast masses, achieving a mean Dice coefficient of 0.82, a true positive fraction (TPF) of 0.84, and a false positive fraction (FPF) of 0.01. By avoiding positioning of an initial seed, the algorithm is able to segment images in real time (13–55 ms per image), and can have potential clinical applications. The algorithm is at par with a conventional seeded algorithm, which had a mean Dice coefficient of 0.84 and performs significantly better (P< 0.0001) than the original U-net algorithm.</p></div

Compatibility and Competition Between European and American Corporate Governances: Which Model of Capitalism?

<p>(a) B-mode US image. (b) CUSE shear wave speed map. The vertical extensions of the high shear wave speeds along the mass location demonstrate the effects of calcifications.</p

Number of patients with benign pathologies.

<p>Number of patients with benign pathologies.</p

Automated and real-time segmentation of suspicious breast masses using convolutional neural network - Fig 9

<p>(a) B-mode Image of invasive mammary carcinoma. (b) Manually segmented boundary is shown in red, Multi U-net predicted boundary is shown in blue, the DRLS predicted boundary is shown in green and original U-net is shown in cyan.</p

BI-RADS distribution of patients in training/validation and test sets.

<p>BI-RADS distribution of patients in training/validation and test sets.</p

Boxplot showing the performance of Multi U-net and DRLS algorithm for (a) Dice coefficient, (b) TPF, and (c) FPF for BI-RADS 3, 4 and 5. BI-RADS indicate Breast Imaging Reporting and Data System.

<p>TPF indicates true positive fraction; FPF indicates false positive fraction.</p

Automated and real-time segmentation of suspicious breast masses using convolutional neural network - Fig 11

<p>(a) Bar plot comparing Dice coefficient between original U-net and ten folds of Multi U-net. Each fold is evaluated 5 times to show the variance within each fold. (b) Error bars showing the increasing performance of U-net as more models are included in majority voting. Five different U-nets models are evaluated to show the variance.</p

Number of patients with malignant pathologies.

<p>Number of patients with malignant pathologies.</p