Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning-Reference-Cited by-同舟云学术

Coronary Centerline Extraction via Optimal Flow Paths and CNN Path Pruning

Published:2016 Issue: Volume: Page:317-325
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Gülsün Mehmet A.,Funka-Lea Gareth,Sharma Puneet,Rapaka Saikiran,Zheng Yefeng

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-319-46726-9_37

Reference15 articles.

1. Bekkers, E., Duits, R., Berendschot, T., Ter Haar Romeny, B.: A multi-orientation analysis approach to retinal vessel tracking. J. Math. Imaging Vis. 49(3), 583–610 (2014)

2. Bouix, S., Siddiqi, K., Tannenbaum, A.: Flux driven automatic centerline extraction. MedIA 9(3), 209–221 (2005)

3. Lecture Notes in Computer Science;D Breitenreicher,2013

4. Cetin, S., Demir, A., Yezzi, A., Degertekin, M., Unal, G.: Vessel tractography using an intensity based tensor model with branch detection. IEEE T-MI. 32(2), 348–363 (2013)

5. Coenen, A., et al.: Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. Radiology 274(3), 674–683 (2015)

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