An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images.
An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images.
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DOI：
10.1002/mp.12920
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发表时间：
2018-06
期刊：
影响因子：
3.8
通讯作者：
Sechopoulos I
中科院分区：
文献类型：
--
作者：
Caballo M;Boone JM;Mann R;Sechopoulos I
旨在开发和评价一种新的自动分类算法，以识别专用乳腺CT图像中包含皮肤、血管、脂肪和纤维腺体组织的体素。该算法将基于强度和区域的分割方法与能量最小化样条和无监督数据挖掘方法相结合，用于对不同组织类型进行分类和分割。乳房皮肤分割是通过区域生长方法实现的，该方法使用来自先前提取的皮肤中心线的约束来增加模型的鲁棒性并降低假阳性率。然后使用能量最小化活动轮廓模型通过包括梯度流和基于区域的特征来对脂肪组织体素进行分类。最后，通过基于自动提取的基于形状的特征的k均值聚类算法将血管与纤维腺组织分离。为了评价算法的准确性，获得了两组15个不同患者的乳腺CT扫描，每组扫描均使用不同的乳腺CT系统和采集设置采集。在有经验的乳腺放射科医生的监督下，对每次扫描的三个切片进行手动分割，并将其视为金标准。与手动分割的比较使用五个相似性指标进行量化：骰子相似系数（DSC）、灵敏度、一致性系数和两个豪斯多夫距离度量。为了评估对图像噪声的鲁棒性，在分别将具有增加的标准偏差（在四个步骤中，从0.01到0.04）的高斯噪声添加到来自第一数据集的另外15个切片之后重复分割。此外，为了评价血管系统分类，对三种不同的造影剂注射前和注射后患者乳腺CT图像进行了分类和比较。最后，DSC还使用来自第一个数据集的10张图像与之前提出的乳腺CT组织分类方法进行定量比较。该算法在对两种乳腺CT系统的不同组织类型进行分类时显示出较高的准确性，第一和第二图像数据集的平均DSC分别为95%和90%。此外，它被证明是鲁棒的图像噪声与图像噪声的鲁棒性为85%，83%，79%和71%的图像与四个增加的噪声水平损坏。对于测试数据集，先前用于乳腺组织分类的方法导致87%的平均全局DSC，而我们的方法导致94.5%的全局平均DSC。所提出的算法导致准确和强大的乳腺组织分类，没有事先训练或阈值设置。潜在的应用包括乳腺密度定量和组织模式表征（两者都是癌症发展的生物标志物），基于模拟的辐射剂量分析和基于患者数据的体模设计，这些都可用于进一步的乳腺成像研究。
To develop and evaluate a new automatic classification algorithm to identify voxels containing skin, vasculature, adipose, and fibroglandular tissue in dedicated breast CT images. The proposed algorithm combines intensity‐ and region‐based segmentation methods with energy minimizing splines and unsupervised data mining approaches for classifying and segmenting the different tissue types. Breast skin segmentation is achieved by a region‐growing method which uses constraints from the previously extracted skin centerline to add robustness to the model and to reduce the false positive rate. An energy minimizing active contour model is then used to classify adipose tissue voxels by including gradient flow and region‐based features. Finally, blood vessels are separated from fibroglandular tissue by a k‐means clustering algorithm based on automatically extracted shape‐based features. To evaluate the accuracy of the algorithm, two sets of 15 different patient breast CT scans, each acquired with different breast CT systems and acquisition settings were obtained. Three slices from each scan were manually segmented under the supervision of an experienced breast radiologist and considered the gold standard. Comparisons with manual segmentation were quantified using five similarity metrics: Dice similarity coefficient (DSC), sensitivity, conformity coefficient, and two Hausdorff distance measures. To evaluate the robustness to image noise, the segmentation was repeated after separately adding Gaussian noise with increasing standard deviation (in four steps, from 0.01 to 0.04) to an additional 15 slices from the first dataset. In addition, to evaluate vasculature classification, three different pre‐ and postcontrast injection patient breast CT images were classified and compared. Finally, DSC was also used for quantitative comparisons with previously proposed approaches for breast CT tissue classification using 10 images from the first dataset. The algorithm showed a high accuracy in classifying the different tissue types for both breast CT systems, with an average DSC of 95% and 90% for the first and second image dataset, respectively. Furthermore, it demonstrated to be robust to image noise with a robustness to image noise of 85%, 83%, 79%, and 71% for the images corrupted with the four increasing noise levels. Previous methods for breast tissues classification resulted, for the tested dataset, in an average global DSC of 87%, while our approach resulted in a global average DSC of 94.5%. The proposed algorithm resulted in accurate and robust breast tissue classification, with no prior training or threshold setting. Potential applications include breast density quantification and tissue pattern characterization (both biomarkers of cancer development), simulation‐based radiation dose analysis, and patient data‐based phantom design, which could be used for further breast imaging research.
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影响因子：
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通讯作者：
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