Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading

Hameeteman, K.; Zuluaga, María A.; Freiman, Moti; Joskowicz, Leo; Cuisenaire, O.; Flórez-Valencia, Leonardo; Gülsün, Mehmet A.; Krissian, Karl; Mille, Julien; Wong, Wilbur C.K.; Orkisz, Maciej; Tek, Hüseyin; Hoyos, Marcela Hernández; Benmansour, Fethallah; Chung, Albert C. S.; Rozie, S.; Gils, Marjon van; Borne, L. Van den; Sosna, Jacob; Berman, Phillip W.; Cohen, Naftali; Douek, Philippe; Sánchez, Inés; Aissat, M.; Schaap, Michiel; Metz, Coert; Krestin, Gabriël P.; Lugt, Aad van der; Niessen, Wiro J.; Walsum, Theo van

doi:10.1016/j.media.2011.02.004

Cited by 76 publications

(51 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Section 5.4 we apply our approach to simulated 3D data of carotid bifurcation lumina created by adding Gaussian noise to datasets provided by the MICCAI challenge [45].…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Rouchdy

Cohen

2013

Computer Vision and Image Understanding

View full text Add to dashboard Cite

This paper presents new methods to segment thin tree structures, which are, for example present in microglia extensions and cardiac or neuronal blood vessels. Many authors have used minimal cost paths, or geodesics relative to a local weighting potential P, to find a vessel pathway between two end points.We utilize a set of such geodesic paths to find a tubular tree structure by seeking minimal interaction. We introduce a new idea that we call Geodesic Voting or Geodesic Density. The approach consists of computing geodesics from a set of end points scattered in the image which flow toward a given source point. The target structure corresponds to image points with a high geodesic density. The "Geodesic density" is defined at each pixel of the image as the number of geodesics that pass over this pixel. The potential P is defined in such way that it takes low values along the tree structure, therefore geodesics will migrate toward this structure thereby yielding a high geodesic density. We further adapt these methods to segment complex tree structures in a noisy medium and apply them to segment microglia extensions from confocal microscope images as well as vessels.

show abstract

“…In Section 5.4 we apply our approach to simulated 3D data of carotid bifurcation lumina created by adding Gaussian noise to datasets provided by the MICCAI challenge [45].…”

Section: Resultsmentioning

confidence: 99%

“…The simulated 3D data were created by adding Gaussian noise to binary images of carotid bifurcation lumina provided by the MICCAI challenge [45] with different values for the noise variance: v k = k 100 , k = 1, . .…”

Section: Experiments On 3d Datamentioning

confidence: 99%

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Rouchdy

Cohen

2013

Computer Vision and Image Understanding

View full text Add to dashboard Cite

show abstract

“…Despite remarkable robustness, this work is not considering the supra aortic arteries. Recently an extensive study was carried out, exclusively focusing on the carotid artery [8] bifurcation, concluding that accurate automatic segmentation is feasible. Their results are limited to high resolution CTA images.…”

Section: Introductionmentioning

confidence: 99%

Personalized learning-based segmentation of thoracic aorta and main branches for diagnosis and treatment planning

Vitanovski

Ralovich

Ionasec

et al. 2012

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

View full text Add to dashboard Cite

Coarctation of the aorta (CoA), is an obstruction of the aortic arch present in 5 − 8% of congenital heart diseases. For children older than a year, CoA is increasingly treated by aortic stenting instead of surgical repair. In pediatric cardiology, CMR is accepted as the standard non-invasive imaging modality to assess the aortic arch in it's entire spatial context [1]. Interpreting such 3D datasets are required to assess the underlying anatomy during both diagnosis and therapy planning phases. However this process is time consuming and varies with operator skills. Within this study we propose -for the first time in our knowledge -a method of automatic segmentation of the lumen of thoracic aorta and main branches. The personalized model of the aorta and the supra-aortic arteries, automatically estimated from 3D CMR data, will provide better understanding of the complexity of pathology and assist the cardiologist to choose the best treatment and timing of repair. A hierarchical framework based on robust machinelearning algorithms is proposed to estimate the personalized model parameters. Experiments throughout 212 3D CMR volumes demonstrate model estimation error of 3.24 mm and average computation time of 8 sec. combined with clinical evaluation on 32 patients.

show abstract

“…Schaap et al [10] give an extensive survey of the literature and present an experimental comparison of algorithms to extract centerlines of coronary arteries. Hameeteman et al [11] performed a comparison of different algorithms to extract carotid arteries in CTA. Noble and Boukeroui [12] present a survey of different techniques for the segmentation of B-mode US images, including vessels.…”

Section: Introductionmentioning

confidence: 99%

Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound

et al. 2011

View full text Add to dashboard Cite

Purpose The purpose of this paper is to present a methodology to estimate the carotid artery lumen centerlines in ultrasound (US) images obtained in a free-hand examination. Challenging aspects here are speckle noise in US images, artifacts, and the lack of contrast in the direction orthogonal to the US beam direction. Method An algorithm based on a rough lumen segmentation obtained by robust ellipse fitting was developed to deal with these conditions and estimate the lumen center in 2D B-mode scans. In a free-hand sweep examination, continuous image acquisitions are performed through time when the radiologist moves the probe on the patient's neck. The result is a series of images that show 2D cross-sections of the carotid's morphology. A tracking sensor (Flock of Birds) was attached to the probe and both were connected to a PC executing the Stradwin software, which relates spatial information to the acquisition data of the US probe. The spatial information was combined with the 2D lumen center estimates to provide a centerline in 3D. For validation, 19 carotid scans from 15 different patients were scanned, their centerlines calculated by the algorithm and compared with results acquired by manual annotations. Results The average Euclidean distance between both among all the examinations was 0.82 mm. For each examination, the percentage of these Euclidean distances below 2 mm was calculated; the average over all examinations was 92%. Conclusion Automated 3D estimation of carotid artery lumen centerlines in free-hand real-time ultrasound is feasible and can be performed with high accuracy. The algorithm is robust enough to keep the centerlines inside the vessel, even in the absence of contrast in parts of the vessel wall.

show abstract

Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading

Cited by 76 publications

References 41 publications

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Geodesic voting for the automatic extraction of tree structures. Methods and applications

Personalized learning-based segmentation of thoracic aorta and main branches for diagnosis and treatment planning

Estimating 3D lumen centerlines of carotid arteries in free-hand acquisition ultrasound

Contact Info

Product

Resources

About