Computerized detection and classification of cancer on breast ultrasound
Computerized detection and classification of cancer on breast ultrasound
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DOI：
10.1016/s1076-6332(03)00723-2
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发表时间：
2004-05-01
影响因子：
4.8
通讯作者：
Mendelson, EB
中科院分区：
文献类型：
--
作者：
Drukker, K;Giger, ML;Mendelson, EB
理论基础和目标。开发和评估一种两阶段计算机化方法，该方法首先在超声图像上检测可疑区域，然后区分不同的病变类型。检测潜在病变的第一阶段是基于预期的病变形状和边缘特征。在检测阶段之后，利用基于计算机提取的病变特征的贝叶斯神经网络对所有候选病变进行分类。在分类阶段执行和评估了两个单独的任务：第一个分类任务是区分所有实际病变和假阳性检测；第二个分类任务是区分实际癌症和所有其他检测到的候选病变(包括假阳性检测)。神经网络在400例(757幅图像)的数据库上进行训练，其中包括复杂的囊性病变和良恶性病变，并在458例(1740幅图像包括578幅正常图像)的独立数据库上进行测试。在区分所有实际病变和假阳性检测时，使用训练和测试数据集分别获得了0.94和0.91的A(Z)值。对于测试数据集的这种检测+分类方案，在每幅图像0.45次假阳性检测时，患者的敏感度达到90%。事实证明，将癌症与所有其他检测(假阳性加上所有良性病变)区分开来更具挑战性，在训练和测试中分别获得了0.87和0.81的A(Z)值。在检测数据集的癌性病变检测和分类中，患者对每幅图像0.43个假阳性恶性肿瘤的敏感度达到100%。结果表明，这种计算机化的病变检测和分类方法具有良好的性能，并表明了这种系统在临床乳腺超声中的潜力。
Rationale and Objectives. To develop and evaluate a two-stage computerized method that first detects suspicious regions on ultrasound images, and subsequently distinguishes among different lesion types.Materials and Methods. The first stage of detecting potential lesions was based on expected lesion shape and margin characteristics. After the detection stage, all candidate lesions were classified by a Bayesian neural net based on computer-extracted lesion features. Two separate tasks were performed and evaluated at the classification stage: the first classification task was the distinction between all actual lesions and false-positive detections; the second classification task was the distinction between actual cancer and all other detected lesion candidates (including false-positive detections). The neural nets were trained on a database of 400 cases (757 images), consisting of complex cysts and benign and malignant lesions, and tested on an independent database of 458 cases (1,740 images including 578 normal images).Results. In the distinction between all actual lesions and false-positive detections, A(z) values of 0.94 and 0.91 were obtained with the training and testing data sets, respectively. Sensitivity by patient of 90% at 0.45 false-positive detections per image was achieved for this detection-plus-classification scheme for the testing data set. Distinguishing cancer from all other detections (false-positives plus all benign lesions) proved to be more challenging, and A(z) values of 0.87 and 0.81 were obtained during training and testing, respectively. Sensitivity by patient of 100% at 0.43 false-positive malignancies per image was achieved in the detection and classification of cancerous lesions for the testing dataset.Conclusion. The results show promising performance of the computerized lesion detection and classification method, and indicate the potential of such a system for clinical breast ultrasound.