Automatic detection of calcifications in the aorta from CT scans of the abdomen. 3D computer-aided diagnosis
- PMID: 15035514
- DOI: 10.1016/s1076-6332(03)00673-1
Automatic detection of calcifications in the aorta from CT scans of the abdomen. 3D computer-aided diagnosis
Abstract
Rationale and objectives: 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.
Materials and 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.
Conclusion: 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.
Similar articles
-
Extracoronary Thoracic and Coronary Artery Calcifications on Chest CT for Lung Cancer Screening: Association with Established Cardiovascular Risk Factors - The "CT-Risk" Trial.Acad Radiol. 2015 Jul;22(7):880-9. doi: 10.1016/j.acra.2015.03.005. Epub 2015 May 7. Acad Radiol. 2015. PMID: 25957500 Review.
-
Aorta segmentation with a 3D level set approach and quantification of aortic calcifications in non-contrast chest CT.Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:2343-6. doi: 10.1109/EMBC.2012.6346433. Annu Int Conf IEEE Eng Med Biol Soc. 2012. PMID: 23366394 Free PMC article.
-
Common and rare collateral pathways in aortoiliac occlusive disease: a pictorial essay.AJR Am J Roentgenol. 2011 Sep;197(3):W519-24. doi: 10.2214/AJR.10.5896. AJR Am J Roentgenol. 2011. PMID: 21862782 Review.
-
Automated aortic calcium scoring on low-dose chest computed tomography.Med Phys. 2010 Feb;37(2):714-23. doi: 10.1118/1.3284211. Med Phys. 2010. PMID: 20229881
-
Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease.Med Phys. 2007 Apr;34(4):1450-61. doi: 10.1118/1.2710548. Med Phys. 2007. PMID: 17500476
Cited by
-
Objective Methods to Assess Aorto-Iliac Calcifications: A Systematic Review.Diagnostics (Basel). 2024 May 19;14(10):1053. doi: 10.3390/diagnostics14101053. Diagnostics (Basel). 2024. PMID: 38786352 Free PMC article. Review.
-
Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans.Acad Radiol. 2021 Nov;28(11):1491-1499. doi: 10.1016/j.acra.2020.08.022. Epub 2020 Sep 18. Acad Radiol. 2021. PMID: 32958429 Free PMC article.
-
Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering.Comput Biol Med. 2020 May;120:103751. doi: 10.1016/j.compbiomed.2020.103751. Epub 2020 Apr 10. Comput Biol Med. 2020. PMID: 32421652 Free PMC article.
-
Image-Based Cardiac Diagnosis With Machine Learning: A Review.Front Cardiovasc Med. 2020 Jan 24;7:1. doi: 10.3389/fcvm.2020.00001. eCollection 2020. Front Cardiovasc Med. 2020. PMID: 32039241 Free PMC article. Review.
-
Abdominal aortic calcification (AAC) and ankle-brachial index (ABI) predict health care costs and utilization in older men, independent of prevalent clinical cardiovascular disease and each other.Atherosclerosis. 2020 Feb;295:31-37. doi: 10.1016/j.atherosclerosis.2020.01.012. Epub 2020 Jan 19. Atherosclerosis. 2020. PMID: 32000096 Free PMC article.
Publication types
MeSH terms
LinkOut - more resources
Other Literature Sources
Medical