Spatial Attention Lesion Detection on Automated Breast Ultrasound

2019 ◽  
pp. 216-227
Author(s):  
Feiqian Wang ◽  
Xiaotong Liu ◽  
Buyue Qian ◽  
Litao Ruan ◽  
Rongjian Zhao ◽  
...  
Keyword(s):  
Spatial Attention ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
Automated Breast Ultrasound

scholarly journals SOFIA: A Novel Automated Breast Ultrasound System Used on Patients in the Prone Position: A Pilot Study on Lesion Detection in Comparison to Handheld Grayscale Ultrasound

2018 ◽  
Vol 78 (05) ◽  
pp. 499-505 ◽  
Author(s):  
André Farrokh ◽  
Harika Erdönmez ◽  
Fritz Schäfer ◽  
Nicolai Maass
Keyword(s):  
Prone Position ◽  
Recall Rate ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
The Novel ◽  
Scanning Time ◽  
High Recall Rate ◽  
Study Population ◽  
Sensitivity Specificity ◽  
Automated Breast Ultrasound

Abstract Introduction Most of the currently available automated breast ultrasound systems require patients to be in the supine position. Previous data, however, show a high recall rate with this method due to artifacts. The novel automated breast ultrasound scanner SOFIA scans the breast with the patient in a prone position, resulting in even compression of breast tissue. We present our initial results with this examination method. Material and Methods 63 patients were analyzed using a handheld B-mode ultrasound. In cases of BI-RADS 1, 2 or 5, a SOFIA scan was performed. Sensitivity, specificity and accuracy were calculated. Interobserver agreement was evaluated using Cohenʼs kappa. The duration of the scan was measured for both methods. Results No BI-RADS 5 lesion was missed with SOFIA. The SOFIA had an additional recall rate of 16.67% compared to B-mode ultrasound. The sensitivity, specificity and accuracy of SOFIA was 100, 83.33 and 88.89%, respectively. Cohenʼs kappa showed substantial agreement (κ = 0.769) between examiner 1 (B-mode) and examiner 2 (SOFIA). The mean scan duration for the B-mode system and the SOFIA system was 24.21 minutes and 12.94 minutes, respectively. In four cases, D-cup breasts were not scanned in their entirety. Conclusion No cancer was missed when SOFIA was used in this preselected study population. The scanning time was approximately half of that required for B-mode ultrasound. The additional unnecessary recall rate was 16.67%. Larger D cup-size breasts were difficult to position and resulted in an incomplete image in four cases.

scholarly journals Automated breast ultrasound (ABUS) as a screening tool: initial experience

2019 ◽  
Vol 50 (1) ◽  
Author(s):  
Amera Abd Elsalam Mostafa ◽  
Mohamed Adel Eltomey ◽  
Ashraf Mohammed Elaggan ◽  
Amel A. Hashish
Keyword(s):  
Breast Cancer ◽  
Screening Tool ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
Screening Methods ◽  
Initial Experience ◽  
Major Health Problem ◽  
Dense Breasts ◽  
Significant Difference ◽  
Automated Breast Ultrasound

Abstract Background Breast cancer is a major health problem, being the most common cancer in women. Early detection of breast cancer aims to the reduction of mortality and morbidity rates. Conventional screening methods include mammography and ultrasonography; however, both modalities have their limitations. Automated breast ultrasound (ABUS) is a recent technological advancement in the field of breast imaging having the benefit of standardization of the scans and lack of operator dependence as in conventional handheld ultrasound scans. The aim of this work was to report our initial experience of the added value of ABUS as a breast screening tool. The study included 200 patients who had screening mammograms, ultrasound, and ABUS. Results A significant difference was found between the number of lesions detected by ABUS and conventional ultrasound. A significant difference was found between lesions detected by ABUS and mammography which was most evident in patients with dense breasts. Conclusions ABUS is a valuable tool in the screening of the breast with improved lesion detection, especially in patients with dense breasts.

scholarly journals Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

2021 ◽  
Author(s):  
Iris Allajbeu ◽  
Sarah E Hickman ◽  
Nicholas Payne ◽  
Penelope Moyle ◽  
Kathryn Taylor ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Breast Screening ◽  
Screening Tool ◽  
Imaging Technique ◽  
Breast Ultrasound ◽  
Computer Aided Detection ◽  
Future Perspectives ◽  
Technical Aspects ◽  
Dense Breasts ◽  
Automated Breast Ultrasound

Abstract Purpose of Review Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives. Recent Findings ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics. Summary ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.

Comparison of handheld ultrasound and automated breast ultrasound in women recalled after mammography screening

2016 ◽  
Vol 58 (5) ◽  
pp. 515-520 ◽  
Author(s):  
Roxanna Hellgren ◽  
Paul Dickman ◽  
Karin Leifland ◽  
Ariel Saracco ◽  
Per Hall ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mammography Screening ◽  
Breast Ultrasound ◽  
Breast Volume ◽  
Large Area ◽  
Mammographic Finding ◽  
Automated Breast Volume Scanner ◽  
Handheld Ultrasound ◽  
Study Population ◽  
Automated Breast Ultrasound

Background Automated breast volume scanner (ABVS) is an ultrasound (US) device with a wide scanner that sweeps over a large area of the breast and the acquired transverse images are sent to a workstation for reconstruction and review. Whether ABVS is as reliable as handheld US is, however, still not established. Purpose To compare the sensitivity and specificity of ABVS to handheld breast US for detection of breast cancer, in the situation of recall after mammography screening. Material and Methods A total of 113 women, five with bilateral suspicious findings, undergoing handheld breast US due to a suspicious mammographic finding in screening, underwent additional ABVS. The methods were assessed for each breast and each detected lesion separately and classified into two categories: breasts with mammographic suspicion of malignancy and breasts with a negative mammogram. Results Twenty-six cancers were found in 25 women. In the category of breasts with a suspicious mammographic finding (n = 118), the sensitivity of both handheld US and ABVS was 88% (22/25). The specificity of handheld US was 93.5% (87/93) and ABVS was 89.2% (83/93). In the category of breasts with a negative mammography (n = 103), the sensitivity of handheld US and ABVS was 100% (1/1). The specificity of handheld US was 100% (102/102) and ABVS was 94.1% (96/102). Conclusion ABVS can potentially replace handheld US in the investigation of women recalled from mammography screening due to a suspicious finding. Due to the small size of our study population, further investigation with larger study populations is necessary before the implementation of such practice.

The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study

2021 ◽  
Author(s):  
Xiao Luo PhD ◽  
Min Xu ◽  
Guoxue Tang ◽  
Yi Wang PhD ◽  
Na Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Tissue ◽  
Test Group ◽  
Lesion Size ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
Detection Sensitivity ◽  
Control Group ◽  
Factors Affecting ◽  
Handheld Ultrasound

Objectives: The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy. Methods: Women who underwent ABUS and handheld ultrasound from May 2016 to June 2017 (N = 397) were enrolled and divided into training (n = 163 patients with breast cancer and 33 with benign lesions), test (n = 57) and control (n = 144) groups. A convolutional neural network was optimised to detect lesions in ABUS. The sensitivity and false positives (FPs) were evaluated and compared for different breast tissue compositions, lesion sizes, morphologies and echo patterns. Results: In the training set, with 688 lesion regions (LRs), the network achieved sensitivities of 93.8%, 97.2 and 100%, based on volume, lesion and patient, respectively, with 1.9 FPs per volume. In the test group with 247 LRs, the sensitivities were 92.7%, 94.5 and 96.5%, respectively, with 2.4 FPs per volume. The control group, with 900 volumes, showed 0.24 FPs per volume. The sensitivity was 98% for lesions > 1 cm3, but 87% for those ≤1 cm3 (p < 0.05). Similar sensitivities and FPs were observed for different breast tissue compositions (homogeneous, 97.5%, 2.1; heterogeneous, 93.6%, 2.1), lesion morphologies (mass, 96.3%, 2.1; non-mass, 95.8%, 2.0) and echo patterns (homogeneous, 96.1%, 2.1; heterogeneous 96.8%, 2.1). Conclusions: DL had high detection sensitivity with a low FP but was affected by lesion size. Advances in knowledge: DL is technically feasible for the automatic detection of lesions in ABUS.

scholarly journals Pectoralis muscle metastases from breast cancer in a young patient detected by automated breast ultrasound

2019 ◽  
Vol 21 (2) ◽  
pp. 200
Author(s):  
Anca Ileana Ciurea ◽  
Ioana Boca ◽  
Liliana Rogojan ◽  
Larisa Dorina Ciule ◽  
Cristiana Augusta Ciortea
Keyword(s):  
Breast Cancer ◽  
Magnetic Resonance Imaging ◽  
Rare Condition ◽  
Left Breast ◽  
Breast Ultrasound ◽  
Pectoralis Muscle ◽  
Resonance Imaging ◽  
Ultrasound Guided ◽  
Screening Ultrasound ◽  
Automated Breast Ultrasound

Metastases to the skeletal muscle from breast cancer represent an unusual and rare condition. We present the case of a 27-year-old female with left breast cancer (IDC NST G3) who underwent neoadjuvant chemotherapy followed by conservativesurgery (sectorectomy and lymphadenectomy) and radiation therapy. Two months after the end of radiotherapy she presented with a 2 mm skin lesion and she was referred for a screening ultrasound. The screening automated breast ultrasound (ABUS) revealed local recurrence and pectoralis metastases, lesions evaluated also by magnetic resonance imaging. The diagnosis was confirmed by the ultrasound-guided biopsy.

scholarly journals The concordance in lesion detection and characteristics between the Anatomical Intelligence and conventional breast ultrasound Scan method

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Juan Li ◽  
Hao Wang ◽  
Lu Wang ◽  
Ting Wei ◽  
Minggang Wu ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Lesion Detection ◽  
Ultrasound Scan ◽  
K Value ◽  
Scanning Method ◽  
Scan Time ◽  
Image Characteristics ◽  
Breast Radiologist ◽  
Absolute Agreement ◽  
Excellent Reproducibility

Abstract Background The aim of this study was to investigate the concordance in lesion detection, between conventional Handhold Ultrasound (HHUS) and The Anatomical Intelligence for Breast ultrasound scan method. Result The AI-breast showed the absolute agreement between the resident and an experienced breast radiologist. The ICC for the scan time, number, clockface location, distance to the nipple, largest diameter and mean diameter of the lesion obtained by a resident and an experienced breast radiologist were 0.7642, 0.7692, 0.8651, 0.8436, 0.7502, 0.8885, respectively. The ICC of the both practitioners of AI-breast were 0.7971, 0.7843, 0.9283, 0.8748, 0.7248, 0.8163, respectively. The k value of Anatomical Intelligence breast between experienced breast radiologist and resident in these image characteristics of boundary, morphology, aspect ratio, internal echo, and BI-RADS assessment were 0.7424, 0.7217, 0.6741, 0.6419, 0.6241, respectively. The k value of the two readers of AI-breast were 0.6531, 0.6762, 0.6439, 0.6137, 0.5981, respectively. Conclusion The anatomical intelligent breast US scanning method has excellent reproducibility in recording the lesion location and the distance from the nipple, which may be utilized in the lesions surveillance in the future.

scholarly journals First Proof-of-concept Evaluation of the FUSION-X-US-II Prototype for the Performance of Automated Breast Ultrasound in Healthy Volunteers

2021 ◽  
Author(s):  
Benedikt Schaefgen ◽  
Marija Juskic ◽  
Madeleine Hertel ◽  
Richard G Barr ◽  
Marcus Radicke ◽  
...  
Keyword(s):  
Image Quality ◽  
Healthy Volunteers ◽  
Digital Breast Tomosynthesis ◽  
Breast Ultrasound ◽  
Patient Comfort ◽  
Technical Developments ◽  
Concept Evaluation ◽  
Technical Function ◽  
Breast Diagnostics ◽  
Automated Breast Ultrasound

Abstract Purpose: The FUSION-X-US-II prototype was developed to combine 3D-automated breast ultrasound (ABUS) and digital breast tomosynthesis in a single device without decompressing the breast. We evaluated the technical function, feasibility of the examination workflow, image quality, breast tissue coverage and patient comfort of the ABUS device of the new prototype. Methods: In this prospective feasibility study, the FUSION-X-US-II prototype was used to perform ABUS in 30 healthy volunteers without history of breast cancer. The ABUS images of the prototype were interpreted by a physician with specialization in breast diagnostics. Any detected lesions were measured and classified using BI-RADS® scores. Image quality was rated subjectively by the physician and coverage of the breast was measured. Patient comfort was evaluated by a questionnaire after the examination. Results: 106 scans were performed (61 x CC, 23 x ML, 22 x MLO) in 60 breasts. Image acquisition and processing by the prototype was fast and accurate. Breast coverage by ABUS was approximately 90.8%. 16 breast lesions (all benign, classified as BIRADS® 2) were identified. The examination was tolerated by all patients. Conclusion: The FUSION-X-US-II prototype allows a rapid ABUS scan with mostly high patient comfort. Technical developments resulted in an improvement of quality and coverage compared to previous prototype versions. The results are encouraging for a test of the prototype in a clinical setting in combination with tomosynthesis.

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