The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T ²KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T ²KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×10 ⁶ vs. 106.1×10 ⁶, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
Humans ; Breast Neoplasms/diagnostic imaging* ; Female ; Ultrasonography, Mammary/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Neural Networks, Computer ; Breast/diagnostic imaging*

Early screening is an important means to reduce breast cancer mortality. In order to solve the problem of low breast cancer screening rates caused by limited medical resources in remote and impoverished areas, this paper designs a breast cancer screening system aided with portable ultrasound Clarius. The system automatically segments the tumor area of the B-ultrasound image on the mobile terminal and uses the ultrasound radio frequency data on the cloud server to automatically classify the benign and malignant tumors. Experimental results in this study show that the accuracy of breast tumor segmentation reaches 98%, and the accuracy of benign and malignant classification reaches 82%, and the system is accurate and reliable. The system is easy to set up and operate, which is convenient for patients in remote and poor areas to carry out early breast cancer screening. It is beneficial to objectively diagnose disease, and it is the first time for the domestic breast cancer auxiliary screening system on the mobile terminal.
Breast/pathology* ; Breast Neoplasms/pathology* ; Diagnosis, Computer-Assisted ; Early Detection of Cancer ; Female ; Humans ; Ultrasonography ; Ultrasonography, Mammary/methods*

Breast cancer has now become the leading cancer in women. The development of breast ultrasound artificial intelligence (AI) diagnostic technology is conducive to promoting the precise diagnosis and treatment of breast cancer and alleviating the heavy medical burden due to the unbalanced regional development in China. In recent years, on the basis of improving diagnostic efficiency, AI technology has been continuously combined with various clinical application scenarios, thereby providing more comprehensive and reliable evidence-based suggestions for clinical decision-making. Although AI diagnostic technologies based on conventional breast ultrasound gray-scale images and cutting-edge technologies such as three-dimensional (3D) imaging and elastography have been developed to some extent, there are still technical pain points, diffusion difficulties and ethical dilemmas in the development of AI diagnostic technologies for breast ultrasound.
Artificial Intelligence ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Elasticity Imaging Techniques/methods* ; Female ; Humans ; Ultrasonography, Mammary/methods*

OBJECTIVE: To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses. METHODS: A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard. RESULTS: When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05). CONCLUSION S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.
Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Diagnosis, Differential ; Female ; Humans ; Sensitivity and Specificity ; Ultrasonics ; Ultrasonography ; Ultrasonography, Mammary/methods*

Triple-negative breast cancer is a complex type of breast cancer,the most common malignant tumor in women.Since the early image features of triple-negative breast cancer appear benign tumor with rapid growth,this cancer has progressed into the middle and late stages once diagnosed,which leads to high mortality.Therefore,the diagnosis of triple-negative breast cancer has always been a clinical difficulty.This article summarizes the role of ultrasound in the diagnosis and treatment of triple-negative breast cancer.The extracted multi-mode ultrasound features will facilitate the early detection of this cancer and improve the prognosis of these patients.
Breast Neoplasms/diagnostic imaging* ; Diagnosis, Differential ; Female ; Humans ; Triple Negative Breast Neoplasms/diagnostic imaging* ; Ultrasonography ; Ultrasonography, Mammary

Objective To explore the value of elastography strain ratio(SR)combined with breast ultrasound imaging reporting and data system(BI-RADS-US)in the differential diagnosis of breast nodules.Methods A total of 471 breast nodules(from 471 patients)were reclassified by SR combined with BI-RADS-US.With the pathology results as gold standard,the area under the receiver operating characteristic(ROC)curve(AUC)was employed to evaluate the diagnostic performance,and the sensitivity,specificity,and accuracy were compared between the combined method and BI-RADS-US.Results Among the 471 breast nodules,180 nodules were benign and 291 were malignant.The AUC of the combined method was statistically significantly higher than that of BI-RADS-US(0.798 vs. 0.730;Z= 2.583, P= 0.010).SR,BI-RADS-US,and the combined method for diagnosing breast nodules had the sensitivity of 86.6%,99.0%,and 96.6%,the specificity of 67.2%,47.2%,and 63.3%,and the accuracy of 79.2%,79.2%,and 83.9%,respectively.The combined method increased the specificity from 47.2%(BI-RADS-US)to 63.3%(χ
Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Diagnosis, Differential ; Elasticity Imaging Techniques ; Female ; Humans ; ROC Curve ; Sensitivity and Specificity ; Ultrasonography, Mammary

To obtain ultrasound and thermal tomography images of breast cancer during its growth and to assess the value of thermal tomography in detecting breast cancer. Breast cancer models were established with NOD/SCID mice and SD rats. These animal models were examined by thermal tomography,plain ultrasound,and contrast-enhanced ultrasound. Tumor tissues were stained with CD34 to explore the relationship between tumor heat production and vascular pathology. Thermal tomography detected breast cancer 2-4 days earlier than ultrasound. The expression of CD34 in tumor tissues was increased,along with thickened,increased,and irregular blood vessels. Thermal tomography can detect early breast cancer and is a promising tool for screening breast cancer.
Animals ; Breast Neoplasms ; diagnostic imaging ; Early Diagnosis ; Mice ; Mice, Inbred NOD ; Mice, SCID ; Neoplasms, Experimental ; diagnostic imaging ; Rats ; Rats, Sprague-Dawley ; Tomography ; Ultrasonography, Mammary

Three-dimensional automated breast ultrasonography (ABUS) has been approved for screening Epub ahead of print studies as an adjunct to mammography. ABUS provides proper orientation and documentation, resulting in better reproducibility. Optimal image quality is essential for a proper diagnosis, and high-quality images should be ensured when ABUS is used in clinical settings. Image quality in ABUS is highly dependent on the acquisition procedure. Artifacts can interfere with the visibility of abnormalities, reduce the overall image quality, and introduce clinical and technical problems. Nipple shadow and reverberation artifacts are some of the artifacts frequently encountered in ABUS. Radiologists should be familiar with proper image acquisition techniques and possible artifacts in order to acquire high-quality images.
Artifacts* ; Breast* ; Diagnosis ; Early Detection of Cancer ; Mammography ; Mass Screening ; Nipples ; Ultrasonography, Mammary*

OBJECTIVE: To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). MATERIALS AND METHODS: B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared. RESULTS: When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8–92.5% vs. 82.1–93.1%; p < 0.001), accuracy (77.9–88.9% vs. 86.2–90.9%; p = 0.038), and positive predictive value (PPV) (60.2–83.3% vs. 70.4–85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3–88.8% vs. 86.3–95.0%; p = 0.120) and negative predictive value (91.4–93.5% vs. 92.9–97.3%; p = 0.259). CONCLUSION: Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.
Breast ; Dataset ; Diagnosis ; Humans ; Information Systems ; Learning ; Prospective Studies ; Retrospective Studies ; Sensitivity and Specificity ; Ultrasonography ; Ultrasonography, Mammary

PURPOSE: The aim of this study was to evaluate the diagnostic performance of a computer-aided detection (CAD) system used with automated breast ultrasonography (ABUS) for suspicious lesions detected on breast MRI, and CAD-false lesions. MATERIALS AND METHODS: We included a total of 40 patients diagnosed with breast cancer who underwent ABUS (ACUSON S2000) to evaluate multiple suspicious lesions found on MRI. We used CAD (QVCAD™) in all the ABUS examinations. We evaluated the diagnostic accuracy of CAD and analyzed the characteristics of CAD-detected lesions and the factors underlying false-positive and false-negative cases. We also analyzed false-positive lesions with CAD on ABUS. RESULTS: Of a total of 122 suspicious lesions detected on MRI in 40 patients, we excluded 51 daughter nodules near the main breast cancer within the same quadrant and included 71 lesions. We also analyzed 23 false-positive lesions using CAD with ABUS. The sensitivity, specificity, positive predictive value, and negative predictive value of CAD (for 94 lesions) with ABUS were 75.5%, 44.4%, 59.7%, and 62.5%, respectively. CAD facilitated the detection of 81.4% (35/43) of the invasive ductal cancer and 84.9% (28/33) of the invasive ductal cancer that showed a mass (excluding non-mass). CAD also revealed 90.3% (28/31) of the invasive ductal cancers measuring larger than 1 cm (excluding non-mass and those less than 1 cm). The mean sizes of the true-positive versus false-negative mass lesions were 2.08 ± 0.85 cm versus 1.6 ± 1.28 cm (P < 0.05). False-positive lesions included sclerosing adenosis and usual ductal hyperplasia. In a total of 23 false cases of CAD, the most common (18/23) cause was marginal or subareolar shadowing, followed by three simple cysts, a hematoma, and a skin wart. CONCLUSION: CAD with ABUS showed promising sensitivity for the detection of invasive ductal cancer showing masses larger than 1 cm on MRI.
Breast Neoplasms ; Breast ; Hematoma ; Humans ; Hyperplasia ; Magnetic Resonance Imaging ; Nuclear Family ; Sensitivity and Specificity ; Shadowing (Histology) ; Skin ; Ultrasonography, Mammary ; Warts