Automatic segmentation of 3D micro-CT coronary vascular images.

Abstract:

:Although there are many algorithms available in the literature aimed at segmentation and model reconstruction of 3D angiographic images, many are focused on characterizing only a part of the vascular network. This study is motivated by the recent emerging prospects of whole-organ simulations in coronary hemodynamics, autoregulation and tissue oxygen delivery for which anatomically accurate vascular meshes of extended scale are highly desirable. The key requirements of a reconstruction technique for this purpose are automation of processing and sub-voxel accuracy. We have designed a vascular reconstruction algorithm which satisfies these two criteria. It combines automatic seeding and tracking of vessels with radius detection based on active contours. The method was first examined through a series of tests on synthetic data, for accuracy in reproduced topology and morphology of the network and was shown to exhibit errors of less than 0.5 voxel for centerline and radius detections, and 3 degrees for initial seed directions. The algorithm was then applied on real-world data of full rat coronary structure acquired using a micro-CT scanner at 20 microm voxel size. For this, a further validation of radius quantification was carried out against a partially rescanned portion of the network at 8 microm voxel size, which estimated less than 10% radius error in vessels larger than 2 voxels in radius.

journal_name

Med Image Anal

journal_title

Medical image analysis

authors

Lee J,Beighley P,Ritman E,Smith N

doi

10.1016/j.media.2007.06.012

subject

Has Abstract

pub_date

2007-12-01 00:00:00

pages

630-47

issue

6

eissn

1361-8415

issn

1361-8423

pii

S1361-8415(07)00064-3

journal_volume

11

pub_type

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