Robust Shape Regression for Supervised Vessel Segmentation and its Application to Coronary Segmentation in CTA

Michiel Schaap, Theo van Walsum, Lisan Neefjes, Coert Metz, Ermanno Capuano, Marleen de Bruijne, Wiro Niessen

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Scopus)

Abstract

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 multi-variate 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.
Original languageUndefined/Unknown
Pages (from-to)1974-1986
Number of pages13
JournalIEEE Transactions on Medical Imaging
Volume30
Issue number11
DOIs
Publication statusPublished - 2011

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