Automatic detection of calcifications in the aorta from CT scans of the abdomen. 3D computer-aided diagnosis.
OBJECTIVE
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Isgum
Ivana
I
Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands. ivana@isi.uu.nl
van Ginneken
Bram
B
Olree
Marco
M
D001012:Aorta, Abdominal / Q000000981:diagnostic imaging*; D001018:Aortic Diseases / Q000000981:diagnostic imaging* / Q000453:epidemiology; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000453:epidemiology; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted; D009426:Netherlands; D015588:Observer Variation; D011857:Radiographic Image Interpretation, Computer-Assisted; D012015:Reference Standards; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed