Department of Radiology, The University of Chicago, Chicago, IL 60637, USA.
Med Phys. 2012 Feb;39(2):866-73. doi: 10.1118/1.3678991.
To develop an automated method to detect breast masses on dedicated breast CT (BCT) volumes and to conduct a preliminary evaluation of its performance. This method can be used in a computer-aided detection (CADe) system for noncontrast enhanced BCT.
The database included patient images, which were acquired under an IRB-approved protocol. The database in this study consisted of 132 cases. 50 cases contained 58 malignant masses, and 23 cases contained 24 benign masses. 59 cases did not contain any biopsy-proven lesions. Each case consisted of an unenhanced CT volume of a single breast. First, each breast was segmented into adipose and glandular tissues using a fuzzy c-means clustering algorithm. The glandular breast regions were then sampled at a resolution of 2 mm. At each sampling step, a 3.5-cm(3) volume-of-interest was subjected to constrained region segmentation and 17 characteristic features were extracted, yielding 17 corresponding feature volumes. Four features were selected using step-wise feature selection and merged with linear discriminant analysis trained in the task of distinguishing between normal breast glandular regions and masses. Detection performance was measured using free-response receiver operating characteristic analysis (FROC) with leave-one-case-out evaluation.
The feature selection stage selected features that characterized the shape and margin strength of the segmented region. CADe sensitivity per case was 84% (std = 4.2%) at 2.6 (std = 0.06) false positives per volume, or 6 × 10(-3) per slice (at an average of 424 slices per volume in this data set).
This preliminary study demonstrates the feasibility of our approach for CADe for BCT.
开发一种自动检测专用乳腺 CT（BCT）容积中乳腺肿块的方法，并对其性能进行初步评估。这种方法可以用于非对比增强 BCT 的计算机辅助检测（CADe）系统。
该数据库包含根据 IRB 批准的协议获得的患者图像。本研究的数据库包括 132 例病例。其中 50 例包含 58 个恶性肿块，23 例包含 24 个良性肿块，59 例未包含任何经活检证实的病变。每个病例都包含一个未增强的单侧乳腺 CT 容积。首先，使用模糊 C 均值聚类算法将每个乳房分割为脂肪和腺体组织。然后以 2 毫米的分辨率对腺体乳腺区域进行采样。在每个采样步骤中，一个 3.5cm(3)的感兴趣区域（ROI）都要进行受限区域分割，并提取 17 个特征，产生 17 个对应的特征容积。使用逐步特征选择选择了四个特征，并将其与线性判别分析相结合，用于区分正常乳腺腺体区域和肿块。使用留一病例外评估的自由响应接收器操作特性分析（FROC）测量检测性能。
特征选择阶段选择了用于描述分割区域形状和边界强度的特征。每个病例的 CADe 敏感性为 84%（标准差为 4.2%），假阳性率为每体积 2.6（标准差为 0.06），或每切片 6×10(-3)（在这个数据集的平均每个体积有 424 个切片）。
这项初步研究证明了我们的方法用于 BCT 的 CADe 的可行性。
