Segmentation of the whole breast from low-dose chest CT images
Segmentation of the whole breast from low-dose chest CT images
复制标题
从低剂量胸部 CT 图像中分割整个乳房
DOI：
10.1117/12.2082410
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发表时间：
2015
影响因子：
--
通讯作者：
A. Reeves
中科院分区：
文献类型：
--
作者：
Shuang Liu;M. Salvatore;D. Yankelevitz;C. Henschke;A. Reeves
整个乳房的分割是自动化乳腺病变检测的第一步。它还需要自动评估乳腺密度，这被认为是乳腺癌的一个重要风险因素。在本文中，我们提出了一个全自动的算法来分割整个乳房的低剂量胸部CT图像（LDCT），这已被推荐为年度肺癌筛查测试。LDCT中的自动化全乳房分割和潜在乳房密度读数以及病变检测将为接受LDCT筛查的女性提供有用的信息，特别是那些没有接受乳房X线筛查的女性，通过为她们提供额外的乳腺癌风险指标而无需额外的辐射照射。要解决的两个主要挑战是LDCT中乳房的形状和位置的显著变化范围以及胸肌与腺体组织的分离。所提出的算法实现了强大的整个乳房分割使用解剖指导的规则为基础的方法。通过将分割与由放射科医师在每次扫描的一个轴向切片和两个矢状切片上手动注释的地面实况进行比较，对20个LDCT扫描进行评价。得到的平均Dice系数为0.880，标准差为0.058，表明自动分割算法实现的结果与放射科医师的手动注释一致。
The segmentation of whole breast serves as the first step towards automated breast lesion detection. It is also necessary for automatically assessing the breast density, which is considered to be an important risk factor for breast cancer. In this paper we present a fully automated algorithm to segment the whole breast in low-dose chest CT images (LDCT), which has been recommended as an annual lung cancer screening test. The automated whole breast segmentation and potential breast density readings as well as lesion detection in LDCT will provide useful information for women who have received LDCT screening, especially the ones who have not undergone mammographic screening, by providing them additional risk indicators for breast cancer with no additional radiation exposure. The two main challenges to be addressed are significant range of variations in terms of the shape and location of the breast in LDCT and the separation of pectoral muscles from the glandular tissues. The presented algorithm achieves robust whole breast segmentation using an anatomy directed rule-based method. The evaluation is performed on 20 LDCT scans by comparing the segmentation with ground truth manually annotated by a radiologist on one axial slice and two sagittal slices for each scan. The resulting average Dice coefficient is 0.880 with a standard deviation of 0.058, demonstrating that the automated segmentation algorithm achieves results consistent with manual annotations of a radiologist.
影响因子：
4.8
作者：
Reeves, Anthony P.;Biancardi, Alberto M.;Clarke, Laurence P.
通讯作者：
Clarke, Laurence P.