Isgum Ivana, van Ginneken Bram, Olree Marco
Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
Acad Radiol. 2004 Mar;11(3):247-57. doi: 10.1016/s1076-6332(03)00673-1.
Automated detection and quantification of arterial calcifications can facilitate epidemiologic research and, eventually, the use of full-body calcium scoring in clinical practice. An automatic computerized method to detect calcifications in CT scans is presented.
Forty abdominal CT scans have been randomly selected from clinical practice. They all contained contrast material and belonged to one of four categories: containing "no," "small," "moderate," or "large" amounts of arterial calcification. There were ten scans in each category. The experiments were restricted to the vertical range from the point where the superior mesenteric artery branches off of the descending aorta until the first bifurcation of the iliac arteries. The automatic method starts by extracting all connected objects above 220 Hounsfield units (HU) from the scan. These objects include all calcifications, as well as bony structures and contrast material. To distinguish calcifications from non-calcifications, a number of features are calculated for each object. These features are based on the object's size, location, shape characteristics, and surrounding structures. Subsequently a classification of each object is performed in two stages. First the probability that an object represents a calcification is computed assuming a multivariate Gaussian distribution for the calcifications. Objects with low probability are discarded. The remaining objects are then classified into calcifications and non-calcifications using a 5-nearest-neighbor classifier and sequential forward feature selection. Based on the total volume of calcifications determined by the system, the scan is assigned to one of the four categories mentioned above.
The 40 scans contained a total of 249 calcifications as determined by a human observer. The method detected 209 calcifications (sensitivity 83.9%) at the expense of on average 1.0 false-positive object per scan. The correct category label was assigned to 30 scans and only 2 scans were off by more than one category. Most incorrect classifications can be attributed to the presence of contrast material in the scans.
It is possible to identify the majority of arterial calcifications in abdominal CT scans in a completely automatic fashion with few false positive objects, even if the scans contain contrast material.
动脉钙化的自动检测与定量分析有助于流行病学研究，并最终推动全身钙评分在临床实践中的应用。本文介绍了一种用于在CT扫描中检测钙化的自动计算机化方法。
从临床实践中随机选取40例腹部CT扫描。这些扫描均含有造影剂，且属于以下四类之一：含“无”、“少量”、“中度”或“大量”动脉钙化。每类有10例扫描。实验局限于从肠系膜上动脉从降主动脉分支处到髂动脉第一次分叉的垂直范围。自动方法首先从扫描中提取所有高于220亨氏单位（HU）的连通物体。这些物体包括所有钙化以及骨质结构和造影剂。为了区分钙化与非钙化，为每个物体计算了一些特征。这些特征基于物体的大小、位置、形状特征和周围结构。随后分两个阶段对每个物体进行分类。首先，假设钙化呈多元高斯分布，计算物体代表钙化的概率。概率低的物体被舍弃。然后使用5近邻分类器和顺序向前特征选择将剩余物体分类为钙化和非钙化。根据系统确定的钙化总体积，将扫描归入上述四类之一。
经人工观察确定，40例扫描中共有249处钙化。该方法检测到209处钙化（灵敏度83.9%），代价是平均每次扫描有1.0个假阳性物体。正确的类别标签被分配给30例扫描，只有2例扫描的分类偏差超过一类。大多数错误分类可归因于扫描中存在造影剂。
即使扫描中含有造影剂，也能够以完全自动的方式识别腹部CT扫描中的大多数动脉钙化，且假阳性物体较少。
