Automated Whole Breast Ultrasound Research Articles

Comparison characteristic visibility of the lesions with automated whole breast ultrasound and handheld breast ultrasound in screening situation

Background: Screening mammography and additional ultrasound are effective in detecting occult cancer. We know that handheld breast ultrasound (HHUS) depends on the operator. In comparison, automated whole breast ultrasound (ABUS) decreases these disadvantages of ultrasound procedures and can increase its sensitivity to cancer detection rates, but the results showed such studies, especially masses’ characteristics, are different. Our study wants to evaluate the features of the lesions in all aspects of the masses by using ABUS compared to HHUS to increase overall interpretation confidence. Objective: Comparison of visible breast lesions between ABUS and HHUS. Materials and Methods: Retrospective analysis was conducted with 168 screening mammography cases, the undergoing ABUS and HHUS interpreted as the detected lesion, mass characteristic and BI-RADS between October 2017 to May 2018. The investigator reviewed the pathologic or the 2-year follow-up from hand-held ultrasound results. The agreement measurements were assessed, using SD, ICC, percent agreement and Cohen kappa coefficient. Results: Comparison of the mass's details between two radiologists by using ABUS, ICCs for the location and individual size of the lesion had good reliability. Localization (κ = 0.81) and BI-RADS (κ = 0.82) showed almost perfect agreement showing substantial agreement for mass margin (κ = 0.78), moderate agreement for mass shape (κ = 0.48) as well as 95% agreement for mass orientation. Intra-rater reliability between two modalities also revealed concordance in both radiologists in important ways for breast mass interpretation. Conclusion: ABUS can detect lesions, give accurate locations, certain mass size and a few characteristics, is acceptable for screening and monitor detected lesions.

3D multi-view tumor detection in automated whole breast ultrasound using deep convolutional neural network

In recent years, automated whole breast ultrasound (ABUS) has drawn attention to breast disease detection and diagnosis applications. However, reviewing ABUS volumes is a time-costing task and some subtle tumors may be missed. In this paper, a 3D multi-view tumor detection method is proposed for ABUS volumes. Firstly, a layer connected feature extraction network is designed for Faster R-CNN. Then, orthogonal multi-view slices are reconstructed and detected using this modified Faster R-CNN to extract 2D candidates. Finally, a 3D multi-view position analysis scheme is designed to fuse 2D detection results and get final 3D bounding boxes. The performance of this proposed method is evaluated on a data set of 158 volumes from 75 patients by 5-fold cross-validation. Experimental results show that our method achieves a sensitivity of 95.06% with 0.57 false positives (FPs) per volume. Compared with existing detection methods, the proposed method is more effective and general.

Self-co-attention neural network for anatomy segmentation in whole breast ultrasound.

The automated whole breast ultrasound (AWBUS) is a new breast imaging technique that can depict the whole breast anatomy. To facilitate the reading of AWBUS images and support the breast density estimation, an automatic breast anatomy segmentation method for AWBUS images is proposed in this study. The problem at hand is quite challenging as it needs to address issues of low image quality, ill-defined boundary, large anatomical variation, etc. To address these issues, a new deep learning encoder-decoder segmentation method based on a self-co-attention mechanism is developed. The self-attention mechanism is comprised of spatial and channel attention module (SC) and embedded in the ResNeXt (i.e., Res-SC) block in the encoder path. A non-local context block (NCB) is further incorporated to augment the learning of high-level contextual cues. The decoder path of the proposed method is equipped with the weighted up-sampling block (WUB) to attain class-specific better up-sampling effect. Meanwhile, the co-attention mechanism is also developed to improve the segmentation coherence among two consecutive slices. Extensive experiments are conducted with comparison to several the state-of-the-art deep learning segmentation methods. The experimental results corroborate the effectiveness of the proposed method on the difficult breast anatomy segmentation problem on AWBUS images.

Fully automated lesion segmentation and visualization in automated whole breast ultrasound (ABUS) images.

The number of breast cancer patients has increased each year, and the demand for breast cancer detection has become quite large. There are many common breast cancer diagnostic tools. The latest automated whole breast ultrasound (ABUS) technology can obtain a complete breast tissue structure, which improves breast cancer detection technology. However, due to the large amount of ABUS image data, manual interpretation is time-consuming and labor-intensive. If there are lesions in multiple images, there may be some omissions. In addition, if further volume information or the three-dimensional shape of the lesion is needed for therapy, it is necessary to manually segment each lesion, which is inefficient for diagnosis. Therefore, automatic lesion segmentation for ABUS is an important issue for guiding therapy. Due to the amount of speckle noise in an ultrasonic image and the low contrast of the lesion boundary, it is quite difficult to automatically segment the lesion. To address the above challenges, this study proposes an automated lesion segmentation algorithm. The architecture of the proposed algorithm can be divided into four parts: (I) volume of interest selection, (II) preprocessing, (III) segmentation, and (IV) visualization. A volume of interest (VOI) is automatically selected first via a three-dimensional level-set, and then the method uses anisotropic diffusion to address the speckled noise and intensity inhomogeneity correction to eliminate shadowing artifacts before the adaptive distance regularization level set method (DRLSE) conducts segmentation. Finally, the two-dimensional segmented images are reconstructed for visualization in the three-dimensional space. The ground truth is delineated by two radiologists with more than 10 years of experience in breast sonography. In this study, three performance assessments are carried out to evaluate the effectiveness of the proposed algorithm. The first assessment is the similarity measurement. The second assessment is the comparison of the results of the proposed algorithm and the Chan-Vese level set method. The third assessment is the volume estimation of phantom cases. In this study, in the 2D validation of the first assessment, the area Dice similarity coefficients of the real cases named cases A, real cases B and phantoms are 0.84±0.02, 0.86±0.03 and 0.92±0.02, respectively. The overlap fraction (OF) and overlap value (OV) of the real cases A are 0.84±0.06 and 0.78±0.04, real case B are 0.91±0.04 and 0.82±0.05, respectively. The overlap fraction (OF) and overlap value (OV) of the phantoms are 0.95±0.02 and 0.92±0.03, respectively. In the 3D validation, the volume Dice similarity coefficients of the real cases A, real cases B and phantoms are 0.85±0.02, 0.89±0.04 and 0.94±0.02, respectively. The overlap fraction (OF) and overlap value (OV) of the real cases A are 0.82±0.06 and 0.79±0.04, real cases B are 0.92±0.04 and 0.85±0.07, respectively. The overlap fraction (OF) and overlap value (OV) of the phantoms are 0.95±0.01 and 0.93±0.04, respectively. Therefore, the proposed algorithm is highly reliable in most cases. In the second assessment, compared with Chan-Vese level set method, the Dice of the proposed algorithm in real cases A, real cases B and phantoms are 0.84±0.02, 0.86±0.03 and 0.92±0.02, respectively. The Dice of Chan-Vese level set in real cases A, real cases B and phantoms are 0.65±0.23, 0.69±0.14 and 0.76±0.14, respectively. The Dice performance of different methods on segmentation shows a highly significant impact (P<0.01). The results show that the proposed algorithm is more accurate than Chan-Vese level set method. In the third assessment, the Spearman's correlation coefficient between the segmented volumes and the corresponding ground truth volumes is ρ=0.929 (P=0.01). In summary, the proposed method can batch process ABUS images, segment lesions, calculate their volumes and visualize lesions to facilitate observation by radiologists and physicians.

Segmentation of breast anatomy for automated whole breast ultrasound images with boundary regularized convolutional encoder–decoder network

Abstract The segmentation of breast anatomical layers in the noisy Automated Whole Breast Ultrasound (AWBUS) images is a very challenging task. A boundary regularized deep convolutional encoder–decoder network (ConvEDNet) is proposed here to address this challenge. The training of ConvEDNet is regularized by the boundary cues, which carry geometrical constraints, with the deep supervision technique for better withstand of intrinsic speckle noise and posterior acoustic shadows in ultrasound images. The boundary regularization is denoted as deep boundary supervision (DBS) throughout this paper. The training of the ConvEDNet is further boosted with the adaptive domain transfer (ADT), which is realized with the bridge of encoder training for an edge detector on ultrasound images. Accordingly, the ADT is a two-stage of domain transfer for better landing the encoder on the ultrasound domain. The ADT can provide better network initialization than either the direct usage of pretrained model from natural images or random scratch. Based on the ADT and DBS techniques, the proposed ConvEDNet method achieves better segmentation performance compared with several classic deep learning segmentation methods on the same set of AWBUS images. The segmentation of breast anatomy may potentially assist the exclusion of false-positives for computer-aided detection to further improve the efficiency of clinical image reading.

Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation

Automated whole breast ultrasound (ABUS) has been widely used as a screening modality for examination of breast abnormalities. Reviewing hundreds of slices produced by ABUS, however, is time consuming. Therefore, in this paper, a fast and effective computer-aided detection system based on 3-D convolutional neural networks (CNNs) and prioritized candidate aggregation is proposed to accelerate this reviewing. First, an efficient sliding window method is used to extract volumes of interest (VOIs). Then, each VOI is estimated the tumor probability with a 3-D CNN, and VOIs with higher estimated probability are selected as tumor candidates. Since the candidates may overlap each other, a novel scheme is designed to aggregate the overlapped candidates. During the aggregation, candidates are prioritized based on estimated tumor probability to alleviate over-aggregation issue. The relationship between the sizes of VOI and target tumor is optimally exploited to effectively perform each stage of our detection algorithm. On evaluation with a test set of 171 tumors, our method achieved sensitivities of 95% (162/171), 90% (154/171), 85% (145/171), and 80% (137/171) with 14.03, 6.92, 4.91, and 3.62 false positives per patient (with six passes), respectively. In summary, our method is more general and much faster than preliminary works and demonstrates promising results.

In women with dense breast tissue, does the addition of automated whole-breast ultrasound to mammography lead to increased invasive testing?

EVIDENCE-BASED ANSWER In women with dense breasts, the addition of automated whole-breast ultrasound (ABUS) improves cancer detection rates but increases false-positive results, leading to more biopsies and surgeries (SOR: C, cohort study and validation study).

Phantom experiments using soft-prior regularization EIT for breast cancer imaging

Objective. A soft-prior regularization (SR) electrical impedance tomography (EIT) technique for breast cancer imaging is described, which shows an ability to accurately reconstruct tumor/inclusion conductivity values within a dense breast model investigated using a cylindrical and a breast-shaped tank. Approach. The SR-EIT method relies on knowing the spatial location of a suspicious lesion initially detected from a second imaging modality. Standard approaches (using Laplace smoothing and total variation regularization) without prior structural information are unable to accurately reconstruct or detect the tumors. The soft-prior approach represents a very significant improvement to these standard approaches, and has the potential to improve conventional imaging techniques, such as automated whole breast ultrasound (AWB-US), by providing electrical property information of suspicious lesions to improve AWB-US’s ability to discriminate benign from cancerous lesions. Main results. Specifically, the best soft-regularization technique found average absolute tumor/inclusion errors of 0.015 S m−1 for the cylindrical test and 0.055 S m−1 and 0.080 S m−1 for the breast-shaped tank for 1.8 cm and 2.5 cm inclusions, respectively. The standard approaches were statistically unable to distinguish the tumor from the mammary gland tissue. An analysis of false tumors (benign suspicious lesions) provides extra insight into the potential and challenges EIT has for providing clinically relevant information. Significance. The ability to obtain accurate conductivity values of a suspicious lesion (>1.8 cm) detected from another modality (e.g. AWB-US) could significantly reduce false positives and result in a clinically important technology.

Computer-Aided Diagnosis for Breast Elastography

Ultrasound elastography is a newly developed technique of imaging the tissue elasticity and it has been used clinically to examine a variety of breast lesions in patients. In general, the breast cancer tissue is harder than the adjacent normal breast tissue. Recently, we have developed a computer-aided diagnosis (CADx) system for 2D/3D stain and shearwave elastography. Several elastographic features and B-mode features are used in the proposed elastography CADx system. Several companies such as Aloka, U-systems, and Siemens have developed the new automated whole breast ultrasound (ABUS) machines to automatically scan the whole breast and save the US images into an image file. There are several hundreds or even thousands of image slices in an ABUS case. Hence, a computer-aided detection (CADe) system is proposed for the ABUS systems to find suspicious frames and locate the approximate positions of tumors in the ABUS images. An ABUS Viewer system developed in our MOEA Technology Development Program for Academia project has been transferred to TaiHao Medical Inc., Taiwan (http://taihaomed.com/) and the TaiHao’s BR-ABVS Viewer has obtained the FDA clearance in 2016.

Imaging Technologies in Breast Cancer Screening Beyond Mammography

Still there is a passionate debate in medical world about the best screening method for Breast Cancer. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the most widely used screening modality, with solid evidence of benefit for women aged 40 to 74 years. Even then it has also undergone increased scrutiny for False-Positives with additional testing which increase radiation dose, cost and anxiety. False-Negatives with false sense of security and potential delay in cancer diagnosis. To overcome these challenges, new imaging technologies for breast cancer screening have been developed, including; X- ray Mammography, Contrast Enhanced Mammography, Digital Mammography, Ultrasound, Automated Whole Breast Ultrasound (AWBU), and Magnetic Resonance Imaging (MRI) are being evaluated. The purpose of this paper is to provide an overview of different medical imaging techniques used in the diagnosis of breast cancer. We compare their effectiveness, advantages, and disadvantages for detecting early-stage breast cancer. We mainly focusing on the comparison of these technologies with mammography for the diagnosis of breast cancer. Here we recommend Digital Mammography is the best available screening method for the early detection of Breast Cancer. Even though the optimal screening will ultimately require a personalized approach. It based on the metrics of cancer risk with selective application of specific screening technologies best suited to the individual’s age, risk, and breast density.

Breast Density Analysis with Automated Whole-Breast Ultrasound: Comparison with 3-D Magnetic Resonance Imaging

In this study, a semi-automatic breast segmentation method was proposed on the basis of the rib shadow to extract breast regions from 3-D automated whole-breast ultrasound (ABUS) images. The density results were correlated with breast density values acquired with 3-D magnetic resonance imaging (MRI). MRI images of 46 breasts were collected from 23 women without a history of breast disease. Each subject also underwent ABUS. We used Otsu's thresholding method on ABUS images to obtain local rib shadow information, which was combined with the global rib shadow information (extracted from all slice projections) and integrated with the anatomy's breast tissue structure to determine the chest wall line. The fuzzy C-means classifier was used to extract the fibroglandular tissues from the acquired images. Whole-breast volume (WBV) and breast percentage density (BPD) were calculated in both modalities. Linear regression was used to compute the correlation of density results between the two modalities. The consistency of density measurement was also analyzed on the basis of intra- and inter-operator variation. There was a high correlation of density results between MRI and ABUS (R2 = 0.798 for WBV, R2 = 0.825 for PBD). The mean WBV from ABUS images was slightly smaller than the mean WBV from MR images (MRI: 342.24 ± 128.08 cm3, ABUS: 325.47 ± 136.16 cm3, p < 0.05). In addition, the BPD calculated from MR images was smaller than the BPD from ABUS images (MRI: 24.71 ± 15.16%, ABUS: 28.90 ± 17.73%, p < 0.05). The intra-operator and inter-operator variant analysis results indicated that there was no statistically significant difference in breast density measurement variation between the two modalities. Our results revealed a high correlation in WBV and BPD between MRI and ABUS. Our study suggests that ABUS provides breast density information useful in the assessment of breast health.

Detectability and Usefulness of Automated Whole Breast Ultrasound in Patients with Suspicious Microcalcifications on Mammography: Comparison with Handheld Breast Ultrasound.

PurposeThe purpose of this study was to prospectively evaluate the detectability and usefulness of automated whole breast ultrasound (AWUS) and to compare it with handheld breast ultrasound (HHUS) in cases with suspicious microcalcifications identified by mammography.MethodsForty-two patients with 43 suspicious microcalcifications (25 malignant and 18 benign) detected by mammography underwent AWUS, HHUS, and histol-ogic examination. With knowledge of the mammographic findings, HHUS was performed to assess the visibility of the microcalcifications and the presence of associated masses or ductal changes. Two radiologists reviewed the AWUS images in consensus using the same methods employed for HHUS. Detectability of AWUS was compared with that of HHUS and was correlated with histologic and mammographic findings.ResultsOf the 43 lesions, 32 (74.4%) were detectable by AWUS and 31 (72.1%) by HHUS. No significant differences in sensitivity were found between the two methods (p=0.998). AWUS detected 96% (24/25) of malignant microcalcifications and 44.4% (8/18) of benign microcalcifications. AWUS was more successful in the detection of malignant vs. benign lesions (96.0% vs. 44.4%, p=0.002), lesions >10 mm vs. ≤10 mm in size (86.7% [26/30] vs. 46.2% [6/13], p=0.009), lesions with a fine pleomorphic or linear shape vs. a round or amorphous or coarse heterogeneous shape (94.7% [18/19] vs. 58.3% [14/24], p=0.021), and lesions associated with a mass or architectural distortion vs. without obvious changes on mammography (100% [19/19] vs. 54.2% [13/24], p=0.022).ConclusionDetectability of AWUS was comparable to that of HHUS in cases where suspicious microcalcifications were identified on mammography. Therefore, AWUS might be helpful in the performance of ultrasound-guided percutaneous procedures for highly suspicious microcalcifications.

Study Review on the Role of Image Analysis on Improving Diagnostic and Therapeutic Breast Cancer Outcomes

This paper examines the critical application of image analysis in improving the diagnosis and management of breast cancer using techniques such as mammography, dedicated breast computed tomography (DBCT), background parenchymal enhancement (BPE), positron emission tomography, optical imaging, molecular imaging, automated whole breast ultrasound (AWBU), mechanical imaging, and optical coherence tomography (OCT). The paper performs a review of previously published studies and research on various imaging techniques currently used in the overall screening and assessment of breasts’ size, volume, composition, and density. Using findings from those studies, the paper demonstrates that such methods have been successfully applied into clinical practice to ameliorate health conditions of breast cancer patients and reduce mortality rate.

Imaging Technologies in Breast Cancer Screening Beyond Mammography

Still there is a passionate debate in medical world about the best screening method for Breast Cancer. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the most widely used screening modality, with solid evidence of benefit for women aged 40 to 74 years. Even then it has also undergone increased scrutiny for False-Positives with additional testing which increase radiation dose, cost and anxiety. False-Negatives with false sense of security and potential delay in cancer diagnosis. To overcome these challenges, new imaging technologies for breast cancer screening have been developed, including; X- ray Mammography, Contrast Enhanced Mammography, Digital Mammography, Ultrasound, Automated Whole Breast Ultrasound (AWBU), and Magnetic Resonance Imaging (MRI) are being evaluated. The purpose of this paper is to provide an overview of different medical imaging techniques used in thediagnosis of breast cancer. We compare their effectiveness, advantages, and disadvantages for detecting early-stage breast cancer. We mainly focusing on the comparison of these technologies with mammography for the diagnosis of breast cancer. Here we recommend early detection of Breast Cancer. Even though the optimal screening will ultimately require a personalized approach. It based on the metrics of cancer risk with selective application of specific screening technologies best suited to the individual’s age, risk, and breast density.

Imaging Technologies in Breast Cancer Screening Beyond Mammography.

Still there is a passionate debate in medical world about the best screening method for Breast Cancer. Early detection is an effective way to diagnose and manage breast cancer. Mammography is the most widely used screening modality, with solid evidence of benefit for women aged 40 to 74 years. Even then it has also undergone increased scrutiny for False-Positives with additional testing which increase radiation dose, cost and anxiety. False-Negatives with false sense of security and potential delay in cancer diagnosis. To overcome these challenges, new imaging technologies for breast cancer screening have been developed, including; X- ray Mammography, Contrast Enhanced Mammography, Digital Mammography, Ultrasound, Automated Whole Breast Ultrasound (AWBU), and Magnetic Resonance Imaging (MRI) are being evaluated. The purpose of this paper is to provide an overview of different medical imaging techniques used in the diagnosis of breast cancer. We compare their effectiveness, advantages, and disadvantages for detecting early-stage breast cancer. We mainly focusing on the comparison of these technologies with mammography for the diagnosis of breast cancer. Here we recommend Digital Mammography is the best available screening method for the early detection of Breast Cancer. Even though the optimal screening will ultimately require a personalized approach. It based on the metrics of cancer risk with selective application of specific screening technologies best suited to the individual’s age, risk, and breast density.

Multi-Dimensional Tumor Detection in Automated Whole Breast Ultrasound Using Topographic Watershed

Automated whole breast ultrasound (ABUS) is becoming a popular screening modality for whole breast examination. Compared to conventional handheld ultrasound, ABUS achieves operator-independent and is feasible for mass screening. However, reviewing hundreds of slices in an ABUS image volume is time-consuming. A computer-aided detection (CADe) system based on watershed transform was proposed in this study to accelerate the reviewing. The watershed transform was applied to gather similar tissues around local minima to be homogeneous regions. The likelihoods of being tumors of the regions were estimated using the quantitative morphology, intensity, and texture features in the 2-D/3-D false positive reduction (FPR). The collected database comprised 68 benign and 65 malignant tumors. As a result, the proposed system achieved sensitivities of 100% (133/133), 90% (121/133), and 80% (107/133) with FPs/pass of 9.44, 5.42, and 3.33, respectively. The figure of merit of the combination of three feature sets is 0.46 which is significantly better than that of other feature sets ( [Formula: see text]). In summary, the proposed CADe system based on the multi-dimensional FPR using the integrated feature set is promising in detecting tumors in ABUS images.

Computer-Aided Multiview Tumor Detection for Automated Whole Breast Ultrasound

Automated whole breast ultrasound (ABUS) has become a popular screening tool in recent years. To reduce the review time and misdetection from ABUS images by physicians, a computer-aided detection (CADe) system for ABUS images based on a multiview method is proposed in this study. A total of 58 pathology-proven lesions from 41 patients were used to evaluate the performance of the system. In the proposed CADe system, the fuzzy c-mean clustering method was applied to detect tumor candidates from these ABUS images. Subsequently, the tumor likelihoods of these candidates could be estimated by a logistic linear regression model based on the intensity, morphology, location, and size features in the transverse, longitudinal, and coronal views. Finally, the multiview tumor likelihoods of the tumor candidates could be obtained from the estimated tumor likelihoods of the three views, and the tumor candidates with high multiview tumor likelihoods were regarded as the detected tumors in the proposed system. The sensitivities of the multiview tumor detection for selecting 5, 10, 20, and 30 tumor candidates with the largest multiview tumor likelihoods were 79.31%, 86.21%, 96.55%, and 98.28%, respectively.

Radiologists' Performance for Detecting Lesions and the Interobserver Variability of Automated Whole Breast Ultrasound

ObjectiveTo compare the detection performance of the automated whole breast ultrasound (AWUS) with that of the hand-held breast ultrasound (HHUS) and to evaluate the interobserver variability in the interpretation of the AWUS.Materials and MethodsAWUS was performed in 38 breast cancer patients. A total of 66 lesions were included: 38 breast cancers, 12 additional malignancies and 16 benign lesions. Three breast radiologists independently reviewed the AWUS data and analyzed the breast lesions according to the BI-RADS classification.ResultsThe detection rate of malignancies was 98.0% for HHUS and 90.0%, 88.0% and 96.0% for the three readers of the AWUS. The sensitivity and the specificity were 98.0% and 62.5% in HHUS, 90.0% and 87.5% for reader 1, 88.0% and 81.3% for reader 2, and 96.0% and 93.8% for reader 3, in AWUS. There was no significant difference in the radiologists' detection performance, sensitivity and specificity (p > 0.05) between the two modalities. The interobserver agreement was fair to good for the ultrasonographic features, categorization, size, and the location of breast masses.ConclusionAWUS is thought to be useful for detecting breast lesions. In comparison with HHUS, AWUS shows no significant difference in the detection rate, sensitivity and the specificity, with high degrees of interobserver agreement.

Computer-Aided Tumor Detection Based on Multi-Scale Blob Detection Algorithm in Automated Breast Ultrasound Images

Automated whole breast ultrasound (ABUS) is an emerging screening tool for detecting breast abnormalities. In this study, a computer-aided detection (CADe) system based on multi-scale blob detection was developed for analyzing ABUS images. The performance of the proposed CADe system was tested using a database composed of 136 breast lesions (58 benign lesions and 78 malignant lesions) and 37 normal cases. After speckle noise reduction, Hessian analysis with multi-scale blob detection was applied for the detection of tumors. This method detected every tumor, but some nontumors were also detected. The tumor like lihoods for the remaining candidates were estimated using a logistic regression model based on blobness, internal echo, and morphology features. The tumor candidates with tumor likelihoods higher than a specific threshold (0.4) were considered tumors. By using the combination of blobness, internal echo, and morphology features with 10-fold cross-validation, the proposed CAD system showed sensitivities of 100%, 90%, and 70% with false positives per pass of 17.4, 8.8, and 2.7, respectively. Our results suggest that CADe systems based on multi-scale blob detection can be used to detect breast tumors in ABUS images.

Automated Whole-Breast Ultrasound: Advancing the Performance of Breast Cancer Screening

Screening has been successful in the early detection of some cancers, including cervical, colon, and breast. However, the success in mortality reduction by screening mammography has been limited in women with mammographically dense tissue. Magnetic resonance imaging has been used with success in the screening of high-risk women, but it is expensive. Also, its use in a screening program requires a contrast medium that has not been tested in humans or animals for long-term safety in humans or animals for repeated biannual or annual injections. Ultrasound also has the potential to be an ideal screening tool because it is relatively inexpensive and requires no injected contrast or ionizing radiation. However, the relatively poor conspicuity of some cancers by hand scanning and the considerable radiologist time necessary limit its use. Automated whole-breast ultrasound (AWBU) allows the radiologist to read the images quickly, at a convenient time, while being free from doing the scan. Two-dimensional AWBU uses a cine loop of axial images, with <1 mm image spacing, which are read on a high-resolution monitor to improve the conspicuity of small cancers. A blinded study of this system combined with screening mammography showed that adding AWBU both doubles overall cancer detection and triples the 1 cm-or-less invasive cancers found in dense-breasted women. As expected, mammography had a significantly greater detection rate of ductal carcinoma in situ than AWBU. As yet no clinical studies of other AWBU systems have been published.