Etsuo Takada

Development of a fully automatic scheme for detection of masses in whole breast ultrasound images.

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.

Relevance of substantia nigra hyperechogenicity and reduced odor identification in idiopathic REM sleep behavior disorder

BACKGROUND Substantia nigra (SN) hyperechogenicity determined by transcranial sonography (TCS) and olfactory dysfunction are common findings in Parkinson disease (PD), which may reveal a prodromal synucleinopathy in idiopathic REM sleep behavior disorder (iRBD). METHODS TCS and the Odor Stick Identification Test for Japanese (OSIT-J) were performed in 34 consecutive patients with iRBD (67.9+/-6.1years), 17 consecutive patients with PD (66.4+/-6.7years), and 21 control group subjects (64.4+/-5.8years). RESULTS There was a significantly increased area of echogenicity in the SN in the iRBD group (0.20+/-0.13cm2) and PD group (0.22+/-0.11cm2) compared with the control group (0.06+/-0.06cm(2)). We found pathological SN hyperechogenicity (0.20cm2) in 41.2% of the iRBD group, 52.6% of the PD group, and 9.5% of the control group. Further, there were abnormal findings of both pathological SN hyperechogenicity (0.20cm2) and functional anosmia or hyposmia in 4 (11.8%) or 9 (26.5%) of the iRBD group subjects, respectively, and 7 (57.9%) or 2 (11.8%) of the PD group subjects, respectively. CONCLUSION Pathological SN hyperechogenic abnormality and functional anosmia in iRBD may be a disease state in the transition to a neurodegenerative disease.

Analysis of elastographic and B-mode features at sonoelastography for breast tumor classification.

The purpose of this study was to evaluate the accuracy of neural network analysis of elastographic features at sonoelastography for the classification of biopsy-proved benign and malignant breast tumors. Sonoelastography of 181 solid breast masses (113 benign and 68 malignant tumors) was performed for 181 patients (mean age, 47 years; range, 24-75 years). After the manual segmentation of the tumors, five elastographic features (strain difference, strain ratio, mean, median and mode) and six B-mode features (orientation, undulation, angularity, average gradient, gradient variance and intensity variance) were computed. A neural network was used to classify tumors by the use of these features. The Students t test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. Area under ROC curve (Az) values of the three elastographic features- mean (0.87), median (0.86) and mode (0.83)-were significantly higher than the Az values for the six B-mode features (0.54-0.69) (p < 0.01). Accuracy, sensitivity, specificity and Az of the neural network for the classification of solid breast tumors were 86.2% (156/181), 83.8% (57/68), 87.6% (99/113) and 0.84 for the elastographic features, respectively, and 82.3% (149/181), 70.6% (48/68), 89.4% (101/113) and 0.78 for the B-mode features, respectively, and 90.6% (164/181), 95.6% (65/68), 87.6% (99/113) and 0.92 for the combination of the elastographic and B-mode features, respectively. We conclude that sonoelastographic images and neural network analysis of features has the potential to increase the accuracy of the use of ultrasound for the classification of benign and malignant breast tumors.

Efficacy of Sonazoid (Perflubutane) for Contrast-Enhanced Ultrasound in the Differentiation of Focal Breast Lesions: Phase 3 Multicenter Clinical Trial

OBJECTIVE The objective of our study was to compare the efficacy of contrast-enhanced ultrasound (CEUS) using the ultrasound contrast agent Sonazoid (perflubutane) with unenhanced ultrasound and supplementary contrast-enhanced MRI in the differential diagnosis (benign vs malignant) of focal breast lesions. The safety of Sonazoid was also assessed in this study. SUBJECTS AND METHODS A total of 127 patients with focal breast lesions were enrolled in this study at five centers in Japan. Three reviewers who were blinded to the patient characteristics independently assessed the ultrasound images and MR images in a randomized sequence. The accuracy, sensitivity, and specificity of CEUS, unenhanced ultrasound, and supplementary contrast-enhanced MRI for the differential diagnosis were compared using generalized estimating equation analyses. Diagnostic confidence was also assessed. RESULTS The accuracy of CEUS was significantly higher than that of unenhanced ultrasound (87.2% vs 65.5%, respectively; p < 0.001). In addition, CEUS showed significantly higher specificity, although the improvement in sensitivity was not statistically significant. The accuracy and specificity were significantly higher with CEUS than with contrast-enhanced MRI, but the improvement in sensitivity was not statistically significant. The area under the curve in a receiver operating characteristic analysis was significantly greater with CEUS than with unenhanced ultrasound. The incidence of adverse events was 11.4% and the incidence of adverse drug reactions was 3.3%. All adverse drug reactions were mild. CONCLUSION CEUS using Sonazoid was confirmed to be superior to unenhanced ultrasound for the differential diagnosis (benign vs malignant) of focal breast lesions in terms of diagnostic accuracy with no serious adverse reactions.

Rapid image stitching and computer‐aided detection for multipass automated breast ultrasound

PURPOSE Breast ultrasound (US) is recently becoming more and more popular for detecting breast lesions. However, screening results in hundreds of US images for each subject. This magnitude of images can lead to fatigue in radiologist, causing failure in the detection of lesions of a subtle nature. In this study, an image stitching technique is proposed for combining multipass images of the whole breast into a series of full-view images, and a fully automatic screening system that works off these images is also presented. METHODS Using the registration technique based on the simple sum of absolute block-mean difference (SBMD) measure, three-pass images were merged into full-view US images. An automatic screening system was then developed for detecting tumors from these full-view images. The preprocessing step was used to reduce the tumor detection time of the system and to improve image quality. The gray-level slicing method was then used to divide images into numerous regions. Finally, seven computerized features--darkness, uniformity, width-height ratio, area size, nonpersistence, coronal area size, and region continuity--were defined and used to determine whether or not each region was a part of a tumor. RESULTS In the experiment, there was a total of 25 experimental cases with 26 lesions, and each case was composed of 252 images (three passes, 84 images/pass). The processing time of the proposed stitching procedure for each case was within 30 s with a Pentium IV 2.0 processor, and the detection sensitivity of the proposed CAD system was 92.3% with 1.76 false positives per case. CONCLUSIONS The proposed automatic screening system can be applied to the whole breast images stitched together via SBMD-based registration in order to detect tumors.

Breast Density Analysis in 3-D Whole Breast Ultrasound Images

The breast density information is one of important factors for estimating the risk in breast cancer detection and early prevention. In this paper, we present two methods, including threshold-based and proportion-based, to automatically analyze the breast density using whole breast ultrasound. The two algorithms are experimented with 32 cases which are scanned from 32 patients using the US machine SSD-5500 with a recent developed scanner ASU-1004 (Aloka, Japan). The experimental results are graded from 4 (extremely dense tissue) to 1 (almost entirely fat), and respectively compared with the majority grades of three radiologists. The accuracy of the threshold-based and proportion-based strategies is 88% and 84% respectively

Computerized mass detection in whole breast ultrasound images: Reduction of false positives using bilateral subtraction technique

The comparison of left and right mammograms is a common technique used by radiologists for the detection and diagnosis of masses. In mammography, computer-aided detection (CAD) schemes using bilateral subtraction technique have been reported. However, in breast ultrasonography, there are no reports on CAD schemes using comparison of left and right breasts. In this study, we propose a scheme of false positive reduction based on bilateral subtraction technique in whole breast ultrasound images. Mass candidate regions are detected by using the information of edge directions. Bilateral breast images are registered with reference to the nipple positions and skin lines. A false positive region is detected based on a comparison of the average gray values of a mass candidate region and a region with the same position and same size as the candidate region in the contralateral breast. In evaluating the effectiveness of the false positive reduction method, three normal and three abnormal bilateral pairs of whole breast images were employed. These abnormal breasts included six masses larger than 5 mm in diameter. The sensitivity was 83% (5/6) with 13.8 (165/12) false positives per breast before applying the proposed reduction method. By applying the method, false positives were reduced to 4.5 (54/12) per breast without removing a true positive region. This preliminary study indicates that the bilateral subtraction technique is effective for improving the performance of a CAD scheme in whole breast ultrasound images.

Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience

PurposeA computerized classification scheme to recognize breast parenchymal patterns in whole breast ultrasound (US) images was developed. A preliminary evaluation of the system performance was performed.MethodsBreast parenchymal patterns were classified into three categories: mottled pattern (MP), intermediate pattern (IP), and atrophic pattern (AP). Each classification was defined as proposed by an experienced physician. A total of 281 image features were extracted from a volume of interest which was automatically segmented. Canonical discriminant analysis with stepwise feature selection was employed for the classification of the parenchymal patterns.ResultsThe classification scheme accuracy was computed to be 83.3% (10/12 cases) in MP cases, 91.7% (22/24 cases) in IP cases, 92.9% (13/14 cases) in AP cases, and 90.0% (45/50 cases) in all the cases.ConclusionsThe feasibility of an automated ultrasonography classifier for parenchymal patterns was demonstrated with promising results in whole breast US images.

Classification of Benign and Malignant Masses in Ultrasound Breast Image Based on Geometric and Echo Features

The aim of this paper is to study the use of geometric and echo features in classifying masses in ultrasound images as benign or malignant. While mammography is very effective in detecting masses and other lesions, breast ultrasound is a valuable adjunct in distinguishing solid and fluid-filled masses where the former is mostly malignant and the latter benign. Six features including two geometric features and four echo features derived from the segmented mass and its neighboring regions are employed in this study. They are the compactness and orientation of the mass, two intensity ratios of the mass and its neighboring regions, homogeneity, and depth-to-width ratio of the mass. Linear discriminant analysis and receiver operating characteristic (ROC) analysis are employed for classification and performance evaluation. The area under the ROC curve (AUC) has a value of 0.940 using all breast masses for training and testing and 0.923 using the leave-one-mass-out cross-validation method. Clinically significance of the results will be evaluated using a larger dataset.

Computer-aided diagnosis of breast color elastography

Ultrasound has been an important imaging technique for detecting breast tumors. As opposed to the conventional B-mode image, the ultrasound elastography is a new technique for imaging the elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicates the soft tissue and the blue one indicates the hard tissue, and the harder tissue usually is classified to malignancy. In this paper, we proposed a CAD system on elastography to measure whether this system is effective and accurate to classify the tumor into benign and malignant. According to the features of elasticity, the color elastography was transferred to HSV color space and extracted meaningful features from hue images. Then the neural network was utilized in multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including 113 benign and 67 malignant cases used to examine the classification. The results of the proposed system showed an accuracy of 83.89%, a sensitivity of 85.07% and a specificity of 83.19%. Compared with the physicians diagnosis, an accuracy of 78.33%, a sensitivity of 53.73% and a specificity of 92.92%, the proposed CAD system had better performance. Moreover, the agreement of the proposed CAD system and the physicians diagnosis was calculated by kappa statistics, the kappa 0.54 indicated there is a fair agreement of observers.