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小儿 CT 图像中肋骨、脊柱和椎管的自动分割。

Automatic segmentation of the ribs, the vertebral column, and the spinal canal in pediatric computed tomographic images.

机构信息

Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada.

出版信息

J Digit Imaging. 2010 Jun;23(3):301-22. doi: 10.1007/s10278-009-9176-x. Epub 2009 Feb 14.

DOI:10.1007/s10278-009-9176-x
PMID:19219504
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3046651/
Abstract

We propose methods to perform automatic identification of the rib structure, the vertebral column, and the spinal canal in computed tomographic (CT) images of pediatric patients. The segmentation processes for the rib structure and the vertebral column are initiated using multilevel thresholding and the results are refined using morphological image processing techniques with features based on radiological and anatomical prior knowledge. The Hough transform for the detection of circles is applied to a cropped edge map that includes the thoracic vertebral structure. The centers of the detected circles are used to derive the information required for the opening-by-reconstruction algorithm used to segment the spinal canal. The methods were tested on 39 CT exams of 13 patients; the results of segmentation of the vertebral column and the spinal canal were assessed quantitatively and qualitatively by comparing with segmentation performed independently by a radiologist. Using 13 CT exams of six patients, including a total of 458 slices with the vertebra from different sections of the vertebral column, the average Hausdorff distance was determined to be 3.2 mm with a standard deviation (SD) of 2.4 mm; the average mean distance to the closest point (MDCP) was 0.7 mm with SD = 0.6 mm. Quantitative analysis was also performed for the segmented spinal canal with three CT exams of three patients, including 21 slices with the spinal canal from different sections of the vertebral column; the average Hausdorff distance was 1.6 mm with SD = 0.5 mm, and the average MDCP was 0.6 mm with SD = 0.1 mm.

摘要

我们提出了在小儿 CT 图像中自动识别肋骨结构、脊柱和椎管的方法。肋骨结构和脊柱的分割过程首先使用多级阈值,然后使用基于放射学和解剖学先验知识的特征的形态图像处理技术进行细化。圆的 Hough 变换应用于包括胸椎体结构的裁剪边缘图。检测到的圆的中心用于推导用于分割椎管的重建算法所需的信息。该方法在 13 名患者的 39 次 CT 检查中进行了测试;通过与放射科医生独立进行的分割进行定量和定性比较,评估了脊柱和椎管分割的结果。使用 6 名患者的 13 次 CT 检查,包括来自脊柱不同节段的总共 458 个带有椎体的切片,平均 Hausdorff 距离为 3.2 毫米,标准差 (SD) 为 2.4 毫米;平均最近点距离 (MDCP) 为 0.7 毫米,标准差 (SD) 为 0.6 毫米。还对来自 3 名患者的 3 次 CT 检查的分割椎管进行了定量分析,包括来自脊柱不同节段的 21 个带有椎管的切片;平均 Hausdorff 距离为 1.6 毫米,标准差 (SD) 为 0.5 毫米,平均 MDCP 为 0.6 毫米,标准差 (SD) 为 0.1 毫米。