Semi-automated carotid lumen segmentation in computed tomography angiography images

Abstract

Carotid artery stenosis causes narrowing of carotid lumens and may lead to brain infarction. The purpose of this study was to develop a semi-automated method of segmenting vessel walls, surrounding tissues, and more importantly, the carotid artery lumen by contrast computed tomography angiography (CTA) images and to define the severity of stenosis and present a three-dimensional model of the carotid for visual inspection.In vivo contrast CTA images of 14 patients (7 normal subjects and 7 patients undergoing endarterectomy) were analyzed using a multi-step segmentation algorithm. This method uses graph cut followed by watershed and Hessian based shortest path method in order to extract lumen boundary correctly without being corrupted in the presence of surrounding tissues. Quantitative measurements of the proposed method were compared with those of manual delineation by independent board-certified radiologists. The results were quantitatively evaluated using spatial overlap surface distance indices. A slightly strong match was shown in terms of dice similarity coefficient (DSC) = 0.87±0.08; mean surface distance (D_msd) = 0.32±0.32; root mean squared surface distance (D_rmssd) = 0.49±0.54 and maximum surface distance (D_max) = 2.14±2.08 between manual and automated segmentation of common, internal and external carotid arteries, carotid bifurcation and stenotic artery, respectively. Quantitative measurements showed that the proposed method has high potential to segment the carotid lumen and is robust to the changes of the lumen diameter and the shape of the stenosis area at the bifurcation site. The proposed method for CTA images provides a fast and reliable tool to quantify the severity of carotid artery stenosis.

Keywords

computed tomography angiography / carotid / atherosclerosis / centerline extraction / segmentation

References

[1] Sinnatamby CS. Last's Anatomy, International Edition[M]. 12th ed. Churchill Livingstone, 2011: 560

[2] Bradac GB. Cerebral angiography normal anatomy and vascular pathology[M]. 2nd ed. Springer-Verlag Berlin Heidelberg, 2014

[3] Alpers BJ, Berry  RG, Paddison RM . Anatomical studies of the circle of Willis in normal brain[J]. AMA Arch Neurol Psychiatry, 1959, 81(4): 409–418.

[4] Chaturvedi S, Bruno  A, Feasby T , , and the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology. Carotid endarterectomy--an evidence-based review: report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology[J]. Neurology, 2005, 65(6): 794–801.

[5] Sanderse M, Marquering  HA, Hendriks EA , Automatic initialization algorithm for carotid artery segmentation in CTA images[J], Med Image Comput Comput Assist Interv, 2005; 846–853.

[6] Hemmati H, Kamli-Asl  A, Talebpour A , Semi-automatic 3D segmentation of carotid lumen in contrast-enhanced computed tomography angiography images[J]. Phys Med, 2015, 31(8): 1098–1104.

[7] Hernandez M, Frangi  AF. Non-parametric geodesic active regions: method and evaluation for cerebral aneurysms segmentation in 3DRA and CTA[J]. Med Image Anal, 2007, 11(3): 224–241.

[8] Vukadinovic D, van Walsum  T, Manniesing R ,  Segmentation of the outer vessel wall of the common carotid artery in CTA[J]. IEEE Trans Med Imaging, 2010, 29(1): 65–76.

[9] Manniesing R, Schaap  M, Rozie S ,  Robust CTA lumen segmentation of the atherosclerotic carotid artery bifurcation in a large patient population[J]. Med Image Anal, 2010, 14(6): 759–769.

[10] Freiman M, Joskowicz  L, Broide N ,  Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation[J]. Int J CARS, 2012, 7(5): 799–812.

[11] Hameeteman K, Zuluaga  MA, Freiman M ,  Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading[J]. Med Image Anal, 2011, 15(4): 477–488.

[12] Comaniciu D, Meer  P. Mean shift: A robust approach toward feature space analysis[J]. IEEE Trans Pattern Anal Mach Intell, 2002, 24(5): 603–619

[13]   Cuisenaire O ,  Virmani S ,  Olszewski ME ,  Fully automated segmentation of carotid and vertebral arteries from contrast enhanced CTA: Proceedings of SPIE[C].SPEI, 2008; 69143–69148.

[14] Frangi AF, Niessen  WJ, Vincken KL ,  Multiscale vessel enhancement filtering[J]. Med Image Comput Comput Assist Interv, 1998, 1496: 130–137

[15] Lindeberg T. Edge detection and ridge detection with automatic scale selection[J]. Int J Comput Vis, 1998, 30(2): 117–156

[16] Hemmati HR, Kamali-asl  AR, Talebpour AR ,  Segmentation of carotid arteries in computed tomography angiography images using fast marching and graph cut methods: 21st Iranian Conference on Electrical Engineering (ICEE), Mashhad, Iran[C]. IEEE: Mashhad, Iran, 2013, 1–5.

[17] Alizadeh M, Zadeh  HS, Maghsoudi OH . Segmentation of small bowel tumors in wireless capsule endoscopy using level set method: 27th International Symposium on Computer-Based Medical Systems (CBMS), New York City, USA[C]. IEEE: Los Alamitos California, USA, 2014, 562–563.

[18] Alizadeh M. Image guided radiation therapy: applications in radiology and endoscopy[J]. Am J Bioengine Biotech, 2016, 2(1): 15–23.

[19] Peng B, Zhang  L, Zhang D ,  Image segmentation by iterated region merging with localized graph cuts[J]. Pattern Recognit, 2011, 44(10-11): 2527–2538

[20] Vincent L, Soille  P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations[J]. IEEE Trans Pattern Anal Mach Intell, 1991, 13(6): 583–598

[21] Alizadeh M, Mohamed  F, Faro S ,  Segmentation of spinal cord in the pediatric spinal Diffusion Tensor MR Imaging: 41st Annual Northeast Bioengineering Conference (NEBEC), Troy, USA[C]. IEEE: Syracuse, USA, 2015, 1–2.

[22] Thomas JB, Antiga  L, Che SL ,  Variation in the carotid bifurcation geometry of young versus older adults: implications for geometric risk of atherosclerosis[J]. Stroke, 2005, 36(11): 2450–2456.

[23] Wong KK, Thavornpattanapong  P, Cheung SC ,  Biomechanical investigation of pulsatile flow in a three-dimensional atherosclerotic carotid bifurcation model[J]. J Mech Med Biol, 2013, 13(1): 1–21

[24] dos Santos FL ,  Joutsen A ,  Terada M ,  A semi-automatic segmentation method for the structural analysis of carotid atherosclerotic plaques by computed tomography angiography[J]. J Atheroscler Thromb, 2014, 21(9): 930–940.

[25] Eskandari H, Talebpour  A, Alizadeh M ,  Polyp detection in wireless capsule endoscopy images by using region-based active contour model: 19th Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran[C]. IEEE: Tehran, Iran, 2013, 305–308.

[26] Maghsoudi OH, Talebpour  A, Zadeh HS ,  Informative and uninformative regions detection in WCE frames, Columbia international publishing[J]. J Adv Comput, 2014, 3(1): 12–34.

[27] Barratt DC, Ariff  BB, Humphries KN ,  Reconstruction and quantification of the carotid artery bifurcation from 3-D ultrasound images[J]. IEEE Trans Med Imaging, 2004, 23(5): 567–583.

[28] Freimana M, Joskowicza  L, Sosnab J . A variational method for vessels segmentation: algorithm and application to liver vessels visualization, Proc of SPIE Medical Imaging[J], 2009, 7261: 72610–72618.

[29] Tek H, Ayvac  A, Comaniciu D . Multi-scale vessel boundary detection, International Workshop on Computer Vision for Biomedical Image Applications (CVBIA), Beijing, China[C]. Springer-Verlag: Berlin, Germany, 2005, 388–398.

[30]   Sato Y, Nakajima  S, Shiraga N ,  Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images[J].Med Image Anal, 1998, 2(2): 143–168.

RIGHTS & PERMISSIONS

2017 2017 by the Journal of Biomedical Research.