Automated 3D Breast Ultrasound Research Articles

Multi-modal artificial intelligence for the combination of automated 3D breast ultrasound and mammograms in a population of women with predominantly dense breasts

ObjectivesTo assess the stand-alone and combined performance of artificial intelligence (AI) detection systems for digital mammography (DM) and automated 3D breast ultrasound (ABUS) in detecting breast cancer in women with dense breasts.Methods430 paired cases of DM and ABUS examinations from a Asian population with dense breasts were retrospectively collected. All cases were analyzed by two AI systems, one for DM exams and one for ABUS exams. A selected subset (n = 152) was read by four radiologists. The performance of AI systems was based on analysis of the area under the receiver operating characteristic curve (AUC). The maximum Youden’s index and its associated sensitivity and specificity were also reported for each AI systems. Detection performance of human readers in the subcohort of the reader study was measured in terms of sensitivity and specificity.ResultsThe performance of the AI systems in a multi-modal setting was significantly better when the weights of AI-DM and AI-ABUS were 0.25 and 0.75, respectively, than each system individually in a single-modal setting (AUC-AI-Multimodal = 0.865; AUC-AI-DM = 0.832, p = 0.026; AUC-AI-ABUS = 0.841, p = 0.041). The maximum Youden’s index for AI-Multimodal was 0.707 (sensitivity = 79.4%, specificity = 91.2%). In the subcohort that underwent human reading, the panel of four readers achieved a sensitivity of 93.2% and specificity of 32.7%. AI-multimodal achieves superior or equal sensitivity as single human readers at the same specificity operating points on the ROC curve.ConclusionMultimodal (ABUS + DM) AI systems for detecting breast cancer in women with dense breasts are a potential solution for breast screening in radiologist-scarce regions.

Pore texture analysis in automated 3D breast ultrasound images for implanted lightweight hernia mesh identification: a preliminary study

BackgroundPrecise visualization of meshes and their position would greatly aid in mesh shrinkage evaluation, hernia recurrence risk assessment, and the preoperative planning of salvage repair. Lightweight (LW) meshes are able to preserve abdominal wall compliance by generating less post-implantation fibrosis and rigidity. However, conventional 3D imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) cannot visualize the LW meshes. Patients sometimes have to undergo a second-look operation for visualizing the mesh implants. The goal of this work is to investigate the potential advantages of Automated 3D breast ultrasound (ABUS) pore texture analysis for implanted LW hernia mesh identification.MethodsIn vitro, the appearances of four different flat meshes in both ABUS and 2D hand-held ultrasound (HHUS) images were evaluated and compared. In vivo, pore texture patterns of 87 hernia regions were analyzed both in ABUS images and their corresponding HHUS images.ResultsIn vitro studies, the imaging results of ABUS for implanted LW meshes are much more visualized and effective in comparison to HHUS. In vivo, the inter-class distance of 40 texture features was calculated. The texture features of 2D sectional plans (axial and sagittal plane) have no significant contribution to implanted LW mesh identification. Significant contribution was observed in coronal plane. However, since the mesh may have spatial variation such as shrinkage after implantation surgery, the inter-class distance of 3D coronal plane pore texture features are bigger than 2D coronal plane, so the contribution of 3D coronal plane pore texture features are more valuable than 2D coronal plane for implanted LW mesh identification. The use of 3D pore texture features significantly improved the robustness of the identification method in distinguishing between LW mesh and fascia.ConclusionsAn innovative new ABUS provides additional pore texture visualization, by separating the LW mesh from the fascia tissues. Therefore, ABUS has the potential to provides more accurate features to characterize pore texture patterns, and ultimately provide more accurate measures for implanted LW mesh identification.

A Technical Note on a Novel Technique for the Evaluation of Breast Excision Specimen: Automated Breast Ultrasound System

Specimen ultrasound is a practical, reliable, and efficient method for detecting ultrasound-only breast abnormalities. This technique was developed as a result of an increasing number of breast lesions being observed on ultrasound-only, particularly in younger women. This procedure can be easily performed in either the radiology department or the operative suite and involves a minimal delay during the operation. In this technical note, we want to share our experience with automated 3D breast ultrasound (ABUS) in specimen imaging and its practicability. ABUS can be used as a new technique in specimen ultrasound and gives information about the lesion margin on coronal images. Keywords: Automated breast ultrasound; Automated 3D breast ultrasound; Specimen imaging

229. Breast Imaging Quality: Conventional Handheld Ultrasound versus Automated 3D Breast Ultrasound System

Purpose Automated 3D Breast Ultrasound (ABUS) as an adjunct to mammography is promising technology in screening especially in women with dense breast parenchyma. We aim to inquire numerically imaging performance of Invenia ABUS (GE Healthcare, WI, USA) in comparison to conventional handheld Ultrasound (US), just to check their similar malignancy detection rates [1] . Methods According to SIRM (Societa Italiana di Radiologia Medica [2] ), using a certified phantom it’s possible to provide a general judgment on the operation of the equipment. So we used a rubber-based tissue mimicking material shown in the figure, UC-551M (ATS Laboratories). This phantom allows to investigate every kind of parameters (line targets, axial and lateral resolution, anechoic targets structures of different sizes and gray scale target structures). The phantom acquisitions were made in clinical mode: ABUS select automatically depth of penetration 3,5/4/5 cm and frequency 10/9/8,18 MHz depending on the size of the breast (small/medium/large); conventional US depth has been optimized in the range 4–5 cm and 7,5–15 MHz by an experienced radiologist depending on the specific insert to display, as suggested by ATS Laboratory [3] . Results The preliminary results are shown in the table. Displayed dynamic range is optimal for all the ultrasound scanners (coefficient of determination R2⩾95%) and the maximum value is ABUS and Samsung score of 99%. Distance measures are coherent, there is no geometric distortion. The analysis of the Point Spread Function is underway with advanced mathematical methods. Conclusions ABUS provides a 3D volume consisting of 330 trans-axial images, whose quality is generally comparable with US 2D images. Download : Download high-res image (269KB) Download : Download full-size image

Lesion Segmentation in Automated 3D Breast Ultrasound: Volumetric Analysis.

Mammography is the gold standard screening technique in breast cancer, but it has some limitations for women with dense breasts. In such cases, sonography is usually recommended as an additional imaging technique. A traditional sonogram produces a two-dimensional (2D) visualization of the breast and is highly operator dependent. Automated breast ultrasound (ABUS) has also been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency, facilitating double reading and comparison with past exams. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the three-dimensional (3D) nature of the images makes the analysis difficult and tedious for radiologists. The goal of this work is to develop a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. The effect of different de-noising methods on segmentation is studied showing a significant impact ([Formula: see text]) on the performance using a dataset of 28 temporal pairs resulting in a total of 56 ABUS volumes. The volumetric analysis is also used to evaluate the performance of the developed framework. A mean Dice Similarity Coefficient of [Formula: see text] with a mean False Positive ratio [Formula: see text] has been obtained. The Pearson correlation coefficient between the segmented volumes and the corresponding ground truth volumes is [Formula: see text] ([Formula: see text]). Similar analysis, performed on 28 temporal (prior and current) pairs, resulted in a good correlation coefficient [Formula: see text] ([Formula: see text]) for prior and [Formula: see text] ([Formula: see text]) for current cases. The developed framework showed prospects to help radiologists to perform an assessment of ABUS lesion volumes, as well as to quantify volumetric changes during lesions diagnosis and follow-up.

Inter-rater reliability and double reading analysis of an automated three-dimensional breast ultrasound system: comparison of two independent examiners.

Breast ultrasound could be a valuable tool complementary to mammography in breast cancer screening. Automated 3D breast ultrasound (ABUS) addresses challenges of hand-held ultrasound and could allow double reading analysis of ultrasound images. This trial assesses the inter-rater reliability and double reading analysis of an ABUS system. To assess the reproducibility and diagnostic validity of the ABUS system, SomoV™, a blinded double reading analysis, was performed in 1019 patients (2038 breasts) by two examiners (examiner A/B) and compared to single reading results, as well as to the reference standard regarding its diagnostic validity. Cohen's kappa coefficients were calculated to measure the inter-rater reliability and agreement of the different diagnostic modalities. Patient comfort and time consumption for image acquisition and reading were analyzed descriptively as secondary objectives. Analysis of inter-rater reliability yielded agreement in 81.6% (κ=0.37; p<0.0001) showing fair agreement. Single reading analysis of SomoV™ exams (examiner A/examiner B) compared to reference standard showed good specificity (examiner A: 88.3%/examiner B: 84.5%), fair inter-rater agreement (examiner A: κ=0.31/examiner B: κ=0.31), and adequate sensitivity (examiner A: 53.1%/examiner B: 64.2%). Double reading analysis yielded good sensitivity and specificity (73.7 and 77.7%). Mammography (n=1911) alone detected 160 of 176 carcinomas (sensitivity 90.1%). Adding SomoV™ to mammography would have detected 12 additional carcinomas, resulting in a higher sensitivity of 97.7%. SomoV™ is a promising technique with good sensitivity, high patient comfort, and fair inter-examiner reliability. It allows double reading analysis that, in combination with mammography, could increase detection rates in breast cancer screening.

Breast cancer detection in automated 3D breast ultrasound using iso-contours and cascaded RUSBoosts

Automated 3D breast ultrasound (ABUS) is a new popular modality as an adjunct to mammography for detecting cancers in women with dense breasts. In this paper, a multi-stage computer aided detection system is proposed to detect cancers in ABUS images. In the first step, an efficient despeckling method called OBNLM is applied on the images to reduce speckle noise. Afterwards, a new algorithm based on isocontours is applied to detect initial candidates as the boundary of masses is hypo echoic. To reduce false generated isocontours, features such as hypoechoicity, roundness, area and contour strength are used. Consequently, the resulted candidates are further processed by a cascade classifier whose base classifiers are Random Under-Sampling Boosting (RUSBoost) that are introduced to deal with imbalanced datasets. Each base classifier is trained on a group of features like Gabor, LBP, GLCM and other features. Performance of the proposed system was evaluated using 104 volumes from 74 patients, including 112 malignant lesions. According to Free Response Operating Characteristic (FROC) analysis, the proposed system achieved the region-based sensitivity and case-based sensitivity of 68% and 76% at one false positive per image.

Automated breast ultrasound for the detection and reconstruction of the breast ductal pattern

Purpose Automated 3D breast ultrasound (ABUS) has shown several advantages over the conventional handheld systems and promising results in screening women with dense breasts. In addition, ABUS seems to offer a unique opportunity for mapping in vivo the normal ducts thus improving our understanding of normal/malignant relationships in the adult female breast. Preliminary results of such a study are reported here. Materials and methods Six lactating, nursing volunteers were scanned with the Invenia™ ABUS system (GE Healthcare, WI, USA). The women were imaged before and after breastfeeding their infants. Ductal patterns were generated from the manual segmentations of two experts. Results were qualitatively compared to published work. Descriptive statistics and the t -test were applied to characterize the population and variabilities. Results Milk filled ducts in lactating women offered a natural contrast agent but empty ducts may also be observed under light breast compression. Most of the ducts were detected and 3D maps were generated. An increased localized activity of milk filled ducts was observed in the lower outer quadrants of all breasts. Ducts from the upper quadrants show involvement in women breastfeeding the longest (11–18 months). Intra- and inter-volunteer variabilities were significant. Conclusion The ABUS system provided sufficient information to detect and segment most of the breast ducts. The observed localized activity of the ducts agrees with results from prior similar studies and raises new important issues regarding the role of duct involution on cancer development. Further testing is justified as is the development of computer algorithms for automated 3D ductal structure generation.

Computer-Aided Detection of Cancer in Automated 3-D Breast Ultrasound

Automated 3-D breast ultrasound (ABUS) has gained a lot of interest and may become widely used in screening of dense breasts, where sensitivity of mammography is poor. However, reading ABUS images is time consuming, and subtle abnormalities may be missed. Therefore, we are developing a computer aided detection (CAD) system to help reduce reading time and prevent errors. In the multi-stage system we propose, segmentations of the breast, the nipple and the chestwall are performed, providing landmarks for the detection algorithm. Subsequently, voxel features characterizing coronal spiculation patterns, blobness, contrast, and depth are extracted. Using an ensemble of neural-network classifiers, a likelihood map indicating potential abnormality is computed. Local maxima in the likelihood map are determined and form a set of candidates in each image. These candidates are further processed in a second detection stage, which includes region segmentation, feature extraction and a final classification. On region level, classification experiments were performed using different classifiers including an ensemble of neural networks, a support vector machine, a k-nearest neighbors, a linear discriminant, and a gentle boost classifier. Performance was determined using a dataset of 238 patients with 348 images (views), including 169 malignant and 154 benign lesions. Using free response receiver operating characteristic (FROC) analysis, the system obtains a view-based sensitivity of 64% at 1 false positives per image using an ensemble of neural-network classifiers.

Computer-Aided Lesion Diagnosis in Automated 3-D Breast Ultrasound Using Coronal Spiculation

A computer-aided diagnosis (CAD) system for the classification of lesions as malignant or benign in automated 3-D breast ultrasound (ABUS) images, is presented. Lesions are automatically segmented when a seed point is provided, using dynamic programming in combination with a spiral scanning technique. A novel aspect of ABUS imaging is the presence of spiculation patterns in coronal planes perpendicular to the transducer. Spiculation patterns are characteristic for malignant lesions. Therefore, we compute spiculation features and combine them with features related to echotexture, echogenicity, shape, posterior acoustic behavior and margins. Classification experiments were performed using a support vector machine classifier and evaluation was done with leave-one-patient-out cross-validation. Receiver operator characteristic (ROC) analysis was used to determine performance of the system on a dataset of 201 lesions. We found that spiculation was among the most discriminative features. Using all features, the area under the ROC curve (A(z)) was 0.93, which was significantly higher than the performance without spiculation features (A(z)=0.90, p=0.02). On a subset of 88 cases, classification performance of CAD (A(z)=0.90) was comparable to the average performance of 10 readers (A(z)=0.87).