Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA
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Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA. / Schaap, Michiel; van Walsum, Theo; Neefjes, Lisan; Metz, Coert; Capuano, Ermanno; de Bruijne, Marleen; Niessen, Wiro.
In: IEEE Transactions on Medical Imaging, Vol. 30, No. 11, 2011, p. 1974-1986 .Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA
AU - Schaap, Michiel
AU - van Walsum, Theo
AU - Neefjes, Lisan
AU - Metz, Coert
AU - Capuano, Ermanno
AU - de Bruijne, Marleen
AU - Niessen, Wiro
PY - 2011
Y1 - 2011
N2 - This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.
AB - This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multivariate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.
U2 - 10.1109/TMI.2011.2160556
DO - 10.1109/TMI.2011.2160556
M3 - Journal article
C2 - 21708497
VL - 30
SP - 1974
EP - 1986
JO - I E E E Transactions on Medical Imaging
JF - I E E E Transactions on Medical Imaging
SN - 0278-0062
IS - 11
ER -
ID: 33950182