Use of the bootstrap technique with small training sets for computer-aided diagnosis in breast ultrasound

The purpose of this study was to test the efficacy of using small training sets in computer-aided diagnostic systems (CAD) and to increase the capabilities of ultrasound (US) technology in the differential diagnosis of solid breast tumors. A total of 263 sonographic images of solid breast nodules, including 129 malignancies and 134 benign nodules, were evaluated by using a bootstrap technique with 10 original training samples. Texture parameters of a region-of-interest (ROI) were resampled with a bootstrap technique and a decision-tree model was used to classify the tumor as benign or malignant. The accuracy was 87.07% (229 of 263 tumors), the sensitivity was 95.35% (123 of 129), the specificity was 79.10% (106 of 134), the positive predictive value was 81.46% (123 of 151), and the negative predictive value was 94.64% (106 of 112). This analysis method provides a second opinion for physicians with high accuracy. The new method shows a potential to be useful in future application of CAD, especially when a large database cannot be obtained for training or a newly developed ultrasonic system has smaller sets of samples.     

基于迁移学习的乳腺肿瘤超声图像智能分类诊断

目的 探讨迁移学习方法对乳腺良恶性肿瘤超声图像分类的价值。方法 回顾性分析经病理证实的447例乳腺肿瘤的超声声像图,采用主成分分析法对原始图像进行分析提取;在Matlab 7.0软件中编程实现迁移学习,将量化的图像特征作为输入数据,利用迁移学习对乳腺良恶性肿瘤进行智能分类。结果 乳腺恶性肿瘤的边缘粗糙度、坚固度、邻域灰度差矩阵粗糙度、肿瘤后方与周围区域回声差异及水平方向高频分量和垂直方向低频分量的直方图能量均明显高于良性肿瘤（P均<0.05）。超声和迁移学习方法诊断乳腺恶性肿瘤的敏感度分别为96.21%（127/132）和96.04%（97/101）,特异度为66.35%（209/315）和98.49%（196/199）,准确率为75.17%（336/447）和97.67%（293/300）。结论 超声图像特征定量化可为识别良恶性乳腺肿瘤提供客观的量化参数;迁移学习可有效对乳腺良恶性肿瘤的声像图进行分类。     

计算机辅助多模态融合超声诊断乳腺良恶性肿瘤

目的 观察利用深度学习（DL）融合常规超声和超声弹性成像诊断乳腺良、恶性肿瘤的效能。方法 利用DL卷积神经网络（CNN）提取乳腺肿瘤超声灰阶与超声弹性特征,并进行多模态融合,评价融合弹性图像或弹性比值等不同信息方式对乳腺良、恶性肿瘤的诊断效能;绘制受试者工作特征（ROC）曲线,计算曲线下面积（AUC）,评估多模态融合模型的诊断效能。结果 多模态融合模型鉴别乳腺良、恶性肿物的效能优于单模态常规超声或弹性模型,其中融合灰阶与弹性图像模型鉴别诊断效能优于融合灰阶与弹性比值模型,分类准确率达93.51%,敏感度为94.88%,特异度为92.25%,AUC达0.975。结论 计算机辅助多模态融合有助于提高超声对乳腺良、恶性肿瘤的诊断效能。     

Ultrasonic Nakagami Imaging: A Strategy to Visualize the Scatterer Properties of Benign and Malignant Breast Tumors

Previous studies have demonstrated the usefulness of the Nakagami parameter in characterizing breast tumors by ultrasound. However, physicians or radiologists may need imaging tools in a clinical setting to visually identify the properties of breast tumors. This study proposed the ultrasonic Nakagami image to visualize the scatterer properties of breast tumors and then explored its clinical performance in classifying benign and malignant tumors. Raw data of ultrasonic backscattered signals were collected from 100 patients (50 benign and 50 malignant cases) using a commercial ultrasound scanner with a 7.5 MHz linear array transducer. The backscattered signals were used to form the B-scan and the Nakagami images of breast tumors. For each tumor, the average Nakagami parameter was calculated from the pixel values in the region-of-interest in the Nakagami image. The receiver operating characteristic (ROC) curve was used to evaluate the clinical performance of the Nakagami image. The results showed that the Nakagami image shadings in benign tumors were different from those in malignant cases. The average Nakagami parameters for benign and malignant tumors were 0.69 ± 0.12 and 0.55 ± 0.12, respectively. This means that the backscattered signals received from malignant tumors tend to be more pre-Rayleigh distributed than those from benign tumors, corresponding to a more complex scatterer arrangement or composition. The ROC analysis showed that the area under the ROC curve was 0.81 ± 0.04 and the diagnostic accuracy was 82%, sensitivity was 92% and specificity was 72%. The results showed that the Nakagami image is useful to distinguishing between benign and malignant breast tumors.     

Computer-aided diagnosis based on speckle patterns in ultrasound images

For breast ultrasound, the scatterer number density from backscattered echo was demonstrated in previous research to be a useful feature for tumor characterization. To take advantage of the scatterer number density in B-mode images, spatial compound imaging was obtained, and the statistical properties of speckle patterns were analyzed in this study for use in distinguishing between benign and malignant lesions. A total of 137 breast masses (95 benign cases and 42 malignant cases) were used in the proposed computer-aided diagnosis (CAD) system. For each mass, the average number of speckle pixels in a region of interest (ROI) was calculated to use the concept of scatterer number density. In addition, the first-order and second-order statistics of the speckle pixels were quantified to obtain the distributions of the pixel values and the spatial relations among the pixels. The performance of the speckle features extracted from each ROI was compared with the performance of the segmentation features extracted from each segmented tumor. As a result, the proposed CAD system using the speckle features achieved an accuracy of 89.1% (122/137); a sensitivity of 81.0% (34/42); and a specificity of 92.6% (88/95). All of the differences between the speckle features and the segmentation features are not statistically significant (p > 0.05). In a receiver operating characteristic (ROC) curve analysis, the Az value, area under ROC curve, of the speckle features was significantly better than the Az value of the segmentation features (0.93 vs. 0.86, p = 0.0359). The performance of this approach supports the notion that the speckle patterns induced by the scatterers in tissues can provide information for classifying tumors. The proposed speckle features, which were extracted readily from drawing an ROI without any preprocessing, also provide a more efficient classification approach than tumor segmentation.     

边界增强像素级多模态声像图融合方法评估良、恶性乳腺肿瘤

目的 观察以边界增强多模态乳腺声像图像素级特征融合方法评估良、恶性乳腺肿瘤性质的价值。方法 基于乳腺肿瘤B型声像图提取边界增强图像,于超声弹性复合声像图中提取纯弹性信息图像。对多模态乳腺肿瘤声像图进行像素级特征融合,形成边界特征增强的融合图像,再以卷积神经网络(CNN)进行分类;评估融合方法分类良、恶性乳腺肿瘤的性能,并与单模态方法、特征级融合方法、无边界增强像素级图像融合方法及其他CNN模型进行对比。结果 边界增强像素级特征融合方法有助于CNN提取乳腺肿瘤特征,分类良、恶性乳腺性能最佳,其分类准确率为85.71%,特异度为85.49%,敏感度为86.16%,模型稳定。结论 边界特征增强像素级多模态声像图融合方法可用于判断良、恶性乳腺肿瘤。     

Retrieval technique for the diagnosis of solid breast tumors on sonogram

We evaluated a series of pathologically proven breast tumors using an image-retrieval technique for classifying benign and malignant lesions. A total of 263 breast tumors (129 malignant and 134 benign) were retrospectively evaluated. The physician located regions-of-interest (ROI) of ultrasonic images and texture parameters (contrast, covariance and dissimilarity) were used in the process of the content-based image-retrieval technique. The accuracy of using the retrieval technique for classifying malignancies was 92.55% (236 of 255), the sensitivity was 94.44% (119 of 126), the specificity was 90.70% (117 of 129), the positive predictive value was 90.84% (119 of 131), and negative predictive value was 94.35% (117 of 124) for the proposed computer-aided diagnostic system. This computer-aided diagnosis system can provide a useful tool and its high negative predictive value could potentially help avert benign biopsies. It is unnecessary to perform any training procedures. This computer-aided diagnosis system can provide a second opinion for a sonographic interpreter; the main advantage in this proposed system is that we do not need any training. Historical cases can be directly added into the database and training of the diagnosis system again is not needed. With the growth of the database, more and more information can be collected and used as reference cases while performing diagnoses. This increases the flexibility of our diagnostic system.     

Stepwise logistic regression analysis of tumor contour features for breast ultrasound diagnosis.

To assist the ultrasound (US) differential diagnosis of solid breast tumors by using stepwise logistic regression (SLR) analysis of tumor contour features, we retrospectively reviewed 111 medical records of digitized US images of breast pathologies. They were pathologically proved benign breast tumors from 40 patients (i.e., 40 fibroadenomas) and malignant breast tumors from 71 patients (i.e., 71 infiltrative ductal carcinomas). Radiologists, before analysis by the computer-aided diagnosis (CAD) system, segmented the tumors manually. The contour features were calculated by measuring the radial length of tumor boundaries. The features selection process was accomplished using a stepwise analysis procedure. Then, an SLR model with contour features was used to classify tumors as benign or malignant. In this experiment, cases were sampled with "leave-one-out" test methods to evaluate the SLR performance using a receiver operating characteristic (ROC) curve. The accuracy of our SLR model with contour features for classifying malignancies was 91.0% (101 of 111 tumors), the sensitivity was 97.2% (69 of 71), the specificity was 80.0% (32 of 40), the positive predictive value was 89.6% (69 of 77), and the negative predictive value was 94.1% (32 of 34). The CAD system using SLR can differentiate solid breast nodules with relatively high accuracy and its high negative predictive value could potentially help inexperienced operators to avoid misdiagnoses. Because the SLR model is trainable, it could be optimized if a larger set of tumor images were supplied.     

Texture Analysis Based on Auto-Mutual Information for Classifying Breast Lesions with Ultrasound

Described here is a novel texture extraction method based on auto-mutual information (AMI) for classifying breast lesions. The objective is to extract discriminating information found in the non-linear relationship of textures in breast ultrasound (BUS) images. The AMI method performs three basic tasks: (i) it transforms the input image using the ranklet transform to handle intensity variations of BUS images acquired with distinct ultrasound scanners; (ii) it extracts the AMI-based texture features in the horizontal and vertical directions from each ranklet image; and (iii) it classifies the breast lesions into benign and malignant classes, in which a support-vector machine is used as the underlying classifier. The image data set is composed of 2050 BUS images consisting of 1347 benign and 703 malignant tumors. Additionally, nine commonly used texture extraction methods proposed in the literature for BUS analysis are compared with the AMI method. The bootstrap method, which considers 1000 bootstrap samples, is used to evaluate classification performance. The experimental results indicate that the proposed approach outperforms its counterparts in terms of area under the receiver operating characteristic curve, sensitivity, specificity and Matthews correlation coefficient, with values of 0.82, 0.80, 0.85 and 0.63, respectively. These results suggest that the AMI method is suitable for breast lesion classification systems.     

乳腺BI-RADS 4类结节超声图像特征分析

目的:对超声诊断乳腺BI-RADS 4类结节的超声图像特征进行分析,并与病理结果对比研究。方法:回顾性分析2017年10月至2018年12月我院383例超声诊断为BI-RADS4类乳腺结节的超声图像特征,以手术或穿刺活检病理为金标准,分析良恶性乳腺结节在形态、边界、边缘、钙化、后方回声等方面的差异。结果:与病理诊断结果比较,383例BI-RADS4类结节中,超声诊断良恶性结节的符合率分别为81.5%与71.1%。良性结节患者平均年龄(46.7±10.5)岁,恶性结节患者平均年龄(55.5±12.4)岁,两组具有显著性差异(P<0.05);绘制ROC曲线得出,以46岁为截断值,其曲线下面积为0.695,诊断的敏感性为69.8%,特异性为60.3%。良性结节与恶性结节的超声图像对比:良性结节多表现为形态规则,边界清晰,边缘光滑、分叶,多不伴钙化;恶性结节多表现为形态不规则、边界模糊,边缘成角、毛刺,多伴微钙化(P<0.05)。良恶性结节间后方回声变化比较无显著统计学差异(P>0.05)。结论:超声诊断BI-RADS4类结节中,良恶性病变具有不同的声像图特征,可为临床医生进行乳腺结节良恶性诊断提供重要参考。     

Computer-Aided Diagnosis for Breast Tumors by Using Vascularization of 3-D Power Doppler Ultrasound

This study assessed the accuracy of three-dimensional (3-D) power Doppler ultrasound in differentiating between benign and malignant breast tumors by using a support vector machine (SVM). A 3-D power Doppler ultrasonography was performed on 164 patients with 86 benign and 78 malignant breast tumors. The volume-of-interest (VOI) in 3-D ultrasound images was automatically generated from three rectangular regions-of-interest (ROI). The vascularization index (VI), flow index (FI) and vascularization-flow index (VFI) on 3-D power-Doppler ultrasound images were evaluated for the entire volume area, computer extracted VOI area and the area outside the VOI. Furthermore, patient's age and VOI volume were also applied for breast tumor classifications. Each ultrasonography in this study was classified as benign or malignant based on the features using the SVM model. All the tumors were sampled using k-fold cross-validation (k = 10) to evaluate the diagnostic performance with receiver operating characteristic (ROC) curves. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of SVM for classifying malignancies were 94%, 69%, 73%, 92% and 81%, respectively. The classification performance in terms of Az value for the ROC curve of the features derived from 3-D power Doppler is 0.91. This study indicates that combining 3-D power Doppler vascularity with patient's age and tumor size offers a good method for differentiating benign andmalignant breast tumors. (E-mail: ylhuang@thu.edu.tw (Y.-L.H.); darren_chen@cch.org.tw (D.-R.C.))     

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.     

DFY-Ⅱ型超声图像定量分析仪对乳腺良恶性肿瘤的鉴别诊断

目的 研究DFY-Ⅱ型超声图像定量分析仪对乳腺良恶性肿瘤的量化诊断方法。方法 采集手术确诊的良恶性乳腺肿瘤各20例的原始超声图像,使用DFY-II型超声图像定量分析仪提取两组图像的纹理特征和灰度特征参数,对比分析两组问各参数的差异。结果 良恶性乳腺肿瘤患者超声图像的熵、平均灰度、平均声强、扭曲度、边缘不规则度及纵横比参数比较,差异有统计学意义（P〈0．05）。结论 DFY-Ⅱ型超声图像定量分析仪可量化乳腺肿瘤的超声图像特征,熵、平均灰度、平均声强、扭曲度、边缘不规则度及纵横比对乳腺良恶性肿块的超声诊断量化有一定参考价值。     

基于形态和灰度特征的乳腺肿瘤B超图像识别

目的为B超诊断乳腺肿瘤建立计算机辅助诊断手段,以降低活检数以及提高诊断的准确性和客观性.方法通过提取良性和恶性肿瘤B超图像的形态特征和灰度特征,包括傅立叶描述子,粗糙度和前后场回声比,组成特征矢量,再用k-均值聚类算法对特征矢量进行分类处理.结果 k-均值聚类算法对良性肿瘤的识别率为89.85%,对恶性肿瘤的识别正确率达78.26%.结论本文中建立的方法能较肉眼更精确地反映良性和恶性肿瘤B超图像的特征,如果再结合医生的临床经验能大大提高乳腺肿瘤的诊断准确性.     

灰阶超声鉴别诊断良、恶性乳腺肿瘤

目的探讨灰阶超声鉴别良、恶性乳腺肿瘤的价值。方法利用改进的Level Set变分模型对126例乳腺肿瘤的超声图像进行分割,提取肿瘤边界,分别计算16个形态特征参数,结合特征参数间的相关性及部分特征参数性质确定特征向量组合,最后用模糊C-均值方法鉴别乳腺肿瘤的良、恶性。结果 126例中,恶性肿瘤50例,良性肿瘤76例。通过Level Set模型得到了较好的分割良、恶性的准确率达80.95%(102/126),其敏感度、特异度、阳性预测值和阴性预测值分别为80.00%(40/50)、81.58%(62/76)、74.07%(40/54)和86.11%(62/72)。结论良、恶性乳腺肿瘤在形态上有较大差异,灰阶超声可有效鉴别乳腺肿瘤的性质。     

彩色多普勒超声在甲状腺结节良恶性鉴别诊断中的应用

目的 探讨彩色多普勒超声在甲状腺结节良恶性鉴别诊断中的应用价值,为甲状腺结节的定性诊断提供可靠依据,减少漏误诊.方法 回顾性分析105例经手术病理证实的甲状腺结节患者的超声资料,并总结其声像图特征.结果 105例病例中,病理诊断良性结节71例,甲状腺癌34例,超声诊断符合率81.9%,误诊18例(17.1%).良恶性结...     

基于跨模态注意力机制特征B型超声与弹性超声融合模块联合诊断乳腺良、恶性肿瘤

目的 设计跨模态注意力机制特征融合模块,观察其用于B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的价值。方法 收集371例接受常规超声检查及超声弹性成像的女性乳腺肿瘤患者、共466处病灶;按3∶1∶1将466组病灶图像分为训练集(n=280)、验证集(n=93)及测试集(n=93)。采用卷积神经网络分支模型分别提取B型超声图像和弹性超声图像特征,之后以基于跨模态注意力机制的多模态特征融合网络进行特征融合,观察其诊断乳腺良、恶性肿瘤的价值。结果 改进后的DenseNet用于B型超声诊断乳腺良、恶性肿瘤的准确率为88.43%,敏感度为88.96%,特异度为87.31%,其效能略优于改进前。基于跨模态注意机制特征融合的B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的准确率为94.23%,敏感度为95.11%,特异度为93.28%,效能优于决策加权融合模型、直接串联融合模型及单模态模型。结论 跨模态注意力机制特征融合模块可在一定程度上提高B型超声与弹性超声联合诊断乳腺良、恶性肿瘤的效能。     

乳腺良恶性肿瘤的高频超声图像特征及误诊分析(附86例分析)

目的:分析乳腺良恶性肿瘤的超声特征及超声误诊原因,提高乳腺肿瘤超声定性诊断的符合率。方法:对86例乳腺肿瘤进行二维超声检查,与病理结果进行对比分析,并分析超声误诊原因。结果:86例乳腺肿瘤中良性34例,恶性52例,肿块检出率100%。超声诊断与病理诊断符合率为89.5%,误诊率为10.5%。良恶性乳腺肿瘤的声像图有明显差异,主要表现在两者边界、色膜、形态、回声分布、后方声衰减、钙化灶有无及形态等方面。结论:高频声像图上乳腺良恶性肿瘤表现有一定差异,为分析乳腺肿瘤性质提供了重要信息。     

Tumor feature visualization with unsupervised learning

Dynamic contrast enhanced magnetic resonance imaging (DCE MRI) is applied for diagnosis and therapy control of breast cancer. The malignancy of a lesion is expressed in the average signal kinetics of selected regions of interest (ROI) representing the lesion. The technique is reported to characterize malignant tumors with high sensitivity and highly variable specificity. Computer-based diagnosis (CAD) systems have been proposed to analyze and classify signal time curve data, extracted from hand selected ROI in the DCE MRI data. In this paper, we apply the self-organizing map (SOM) to a set of time curve feature vectors of single voxels from seven benign lesions and seven malignant tumors. Applying the SOM we are able to project the time curve values of each voxel on a two-dimensional map. The results show, that the SOM is able to visualize the hidden two-dimensional structure of the six-dimensional signal space. Using the trained SOM, we are able to identify voxels with benign or malignant signal characteristics and to visualize lesion cross-sections with pseudo-colors. A comparison with the established three time points method shows that the SOM has clear potential for deriving visualization parameters in DCE MRI analysis.     

超声妇科影像报告和数据系统分类与16层螺旋CT诊断良恶性卵巢肿瘤的价值

目的探讨超声妇科影像报告和数据系统（GI-RADS）分类与16层螺旋CT对良恶性卵巢肿瘤鉴别诊断价值。方法选取2015年1月~2019年8月在我院就诊的卵巢肿瘤患者85例为研究对象,分别进行超声及16层螺旋CT检查,采用GI-RADS系统评价超声声像图表现,并检测其癌胚抗原（CEA）水平。比较超声GI-RADS系统、16层螺旋CT联合CEA联合检查结果与病理学检查结果的一致性;以病理学检查结果为金标准,比较超声GI-RADS系统、16层螺旋CT联合CEA检查诊断鉴别良恶性卵巢肿瘤的灵敏度、特异度、阳性预测值、阴性预测值及诊断准确率,并采用ROC曲线分析超声GI-RADS系统、16层螺旋CT联合CEA检查对良恶性卵巢肿瘤的诊断鉴别价值。结果超声GI-RADS系统联合CEA检查结果与病理学检查结果的一致性（Kappa=0.791）大于16层螺旋CT联合CEA（Kappa=0.487）;超声GI-RADS系统、16层螺旋CT联合CEA联合检查诊断良恶性卵巢肿瘤的灵敏度、特异度、恶性预测值、良性预测值对比,差异无统计学意义（P>0.05）;超声GI-RADS系统联合CEA诊断良恶性卵巢肿瘤的准确率高于16层螺旋CT联合CEA（P < 0.05）;经ROC曲线分析得,超声GI-RADS系统联合CEA诊断良恶性卵巢肿瘤的AUC大于16层螺旋CT联合CEA（P < 0.05）。结论超声GI-RADS系统联合CEA检测对良恶性卵巢肿瘤具有较高的诊断价值,且诊断准确率较高。