Automatic segmentation of coronary tree in CT angiography images

Summary In this study, we proposed an automatic approach to segment the three‐dimensional coronary tree from computed tomography angiography (CTA) image data. Compared to the previous studies, our approach can efficiently locate the coronary root through extracting aorta by using circular Hough tran...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of adaptive control and signal processing Ročník 33; číslo 8; s. 1239 - 1247
Hlavní autoři: Gao, Zhifan, Liu, Xin, Qi, Song, Wu, Wanqing, Hau, William Kongto, Zhang, Heye
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.08.2019
Témata:
Angiography
Aorta
Computed tomography
computed tomography angiography
Confidence intervals
coronary artery disease
Dynamic programming
Hough transformation
Image processing
Image segmentation
Medical imaging
vessel segmentation
ISSN:0890-6327, 1099-1115
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Summary In this study, we proposed an automatic approach to segment the three‐dimensional coronary tree from computed tomography angiography (CTA) image data. Compared to the previous studies, our approach can efficiently locate the coronary root through extracting aorta by using circular Hough transform. Then, we extracted the two‐dimensional coronary borders in multiple projection directions by dynamic programming and used the border information to reconstruct the coronary surface by the spline fitting. The performance of our approach has been validated on the CTA dataset from 50 subjects, with the comparison to the manual segmentation by an experienced medical physician on the corresponding CTA data and X‐ray angiography data. Our experiments have shown that the average bias between our approach and the manual segmentation are 2.44 mm2 with confidence interval [‐2.20 mm2, 6.68 mm2] for cross‐sectional lumen area, 0.84 mm with confidence interval [‐2.76 mm, 4.44 mm] for maximum lumen diameter, and 0.32 mm with confidence interval [‐1.14 mm, 1.78 mm] for minimum lumen diameter. The results can demonstrate the potential effectiveness of our approach in CTA image processing.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0890-6327
1099-1115
DOI:10.1002/acs.2762