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Title: 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 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 {mu}m voxel size. For this, a further validation of radius quantification was carried out against a partially rescanned portion of the network at 8 {mu}m voxel size, whichmore » estimated less than 10% radius error in vessels larger than 2 voxels in radius.« less

Authors:
; ; ;
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL) National Synchrotron Light Source
Sponsoring Org.:
Doe - Office Of Science
OSTI Identifier:
960137
Report Number(s):
BNL-83123-2009-JA
TRN: US201016%%1281
DOE Contract Number:
DE-AC02-98CH10886
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Image Analysis; Journal Volume: 11; Journal Issue: 6
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ACCURACY; ALGORITHMS; AUTOMATION; DETECTION; MORPHOLOGY; OXYGEN; PROCESSING; TOPOLOGY; VALIDATION; COMPUTERIZED TOMOGRAPHY; national synchrotron light source

Citation Formats

Lee,J., Beighley, P., Ritman, E., and Smith, N.. Automatic Segmentation of 3D Micro-CT Coronary Vascular Images. United States: N. p., 2007. Web. doi:10.1016/j.media.2007.06.012.
Lee,J., Beighley, P., Ritman, E., & Smith, N.. Automatic Segmentation of 3D Micro-CT Coronary Vascular Images. United States. doi:10.1016/j.media.2007.06.012.
Lee,J., Beighley, P., Ritman, E., and Smith, N.. Mon . "Automatic Segmentation of 3D Micro-CT Coronary Vascular Images". United States. doi:10.1016/j.media.2007.06.012.
@article{osti_960137,
title = {Automatic Segmentation of 3D Micro-CT Coronary Vascular Images},
author = {Lee,J. and Beighley, P. and Ritman, E. and Smith, N.},
abstractNote = {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 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 {mu}m voxel size. For this, a further validation of radius quantification was carried out against a partially rescanned portion of the network at 8 {mu}m voxel size, which estimated less than 10% radius error in vessels larger than 2 voxels in radius.},
doi = {10.1016/j.media.2007.06.012},
journal = {Medical Image Analysis},
number = 6,
volume = 11,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
}