Int J Comput Assist Radiol Surg - Automated aorta segmentation in low-dose chest CT images.

Tópicos

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Resumo

RPOSE: Abnormalities of aortic surface and aortic diameter can be related to cardiovascular disease and aortic aneurysm. Computer-based aortic segmentation and measurement may aid physicians in related disease diagnosis. This paper presents a fully automated algorithm for aorta segmentation in low-dose non-contrast CT images.METHODS: The original non-contrast CT scan images as well as their pre-computed anatomy label maps are used to locate the aorta and identify its surface. First a seed point is located inside the aortic lumen. Then, a cylindrical model is progressively fitted to the 3D image space to track the aorta centerline. Finally, the aortic surface is located based on image intensity information. This algorithm has been trained and tested on 359 low-dose non-contrast CT images from VIA-ELCAP and LIDC public image databases. Twenty images were used for training to obtain the optimal set of parameters, while the remaining images were used for testing. The segmentation result has been evaluated both qualitatively and quantitatively. Sixty representative testing images were used to establish a partial ground truth by manual marking on several axial image slices.RESULTS: Compared to ground truth marking, the segmentation result had a mean Dice Similarity Coefficient of 0.933 (maximum 0.963 and minimum 0.907). The average boundary distance between manual segmentation and automatic segmentation was 1.39 mm with a maximum of 1.79 mm and a minimum of 0.83 mm.CONCLUSION: Both qualitative and quantitative evaluations have shown that the presented algorithm is able to accurately segment the aorta in low-dose non-contrast CT images.

Resumo Limpo

rpose abnorm aortic surfac aortic diamet can relat cardiovascular diseas aortic aneurysm computerbas aortic segment measur may aid physician relat diseas diagnosi paper present fulli autom algorithm aorta segment lowdos noncontrast ct imagesmethod origin noncontrast ct scan imag well precomput anatomi label map use locat aorta identifi surfac first seed point locat insid aortic lumen cylindr model progress fit d imag space track aorta centerlin final aortic surfac locat base imag intens inform algorithm train test lowdos noncontrast ct imag viaelcap lidc public imag databas twenti imag use train obtain optim set paramet remain imag use test segment result evalu qualit quantit sixti repres test imag use establish partial ground truth manual mark sever axial imag slicesresult compar ground truth mark segment result mean dice similar coeffici maximum minimum averag boundari distanc manual segment automat segment mm maximum mm minimum mmconclus qualit quantit evalu shown present algorithm abl accur segment aorta lowdos noncontrast ct imag

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