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

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

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

Jack Lee,Patricia Beighley,Erik Ritman,Nicolas Smith,Jack Lee,Patricia Beighley,Erik Ritman,Nicolas Smith,

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 20mum voxel size. For this, a further validation of radius quantification was carried out against a partially rescanned portion of the network at 8mum voxel size, which estimated less than 10% radius error in vessels larger than 2 voxels in radius.

Automatic segmentation of 3D micro-CT coronary vascular images. Publishing Authors By Initials

J Lee,P Beighley,E Ritman,N Smith,J Lee,P Beighley,E Ritman,N Smith,

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

PUBLICATION TYPE: Journal Article

Journal: Medical image analysis

VOLUME: 11

Page Numbers: 630-47

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ISSN: 1361-8415

DAY: 1

MONTH: 08

YEAR: 2007

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

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LANGUAGE: eng

NlmUniqueID: 9713490

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AFFILIATION: Bioengineering Institute, Faculty of Engineering, The University of Auckland, Private bag 92019 Auckland, New Zealand.

Country: Netherlands

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MEDLINETA: Med Image Anal

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