Liu Jiamin, Pattanaik Sanket, Yao Jianhua, Turkbey Evrim, Zhang Weidong, Zhang Xiao, Summers Ronald M
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences Department, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Comput Med Imaging Graph. 2014 Oct;38(7):606-12. doi: 10.1016/j.compmedimag.2014.04.007. Epub 2014 May 9.
The widespread use of CT imaging and the critical importance of early detection of epidural masses of the spinal canal generate a scenario ideal for the implementation of a computer-aided detection (CAD) system. Epidural masses can lead to paralysis, incontinence and loss of neurological function if not promptly detected. We present, to our knowledge, the first CAD system to detect epidural masses on CT scans. In this paper, spatially constrained Gaussian mixture model (GMM) and supervoxel-based method are proposed for epidural mass detection. The detection is performed on the Gaussian level or the supervoxel level rather than the voxel level. Cross-validation on 40 patients with epidural masses on body CT showed that the supervoxel-based method yielded a significant improvement of performance (82% at 3 false positives per patient) over the spatially constrained GMM method (55% at 3 false positives per patient).
CT成像的广泛应用以及早期检测椎管硬膜外肿块的至关重要性，为实施计算机辅助检测（CAD）系统创造了理想的条件。硬膜外肿块如果不及时发现，可能导致瘫痪、大小便失禁和神经功能丧失。据我们所知，我们提出了首个用于在CT扫描上检测硬膜外肿块的CAD系统。在本文中，提出了空间约束高斯混合模型（GMM）和基于超体素的方法来进行硬膜外肿块检测。检测是在高斯级别或超体素级别而非体素级别上进行的。对40例身体CT上有硬膜外肿块的患者进行交叉验证表明，基于超体素的方法在性能上（每位患者3例假阳性时为82%）比空间约束GMM方法（每位患者3例假阳性时为55%）有显著提高。
