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      Design and validation of Segment - freely available software for cardiovascular image analysis

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          Abstract

          Background

          Commercially available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. We have already implemented a cardiovascular image analysis software package and released it as freeware for the research community. However, it was distributed as a stand-alone application and other researchers could not extend it by writing their own custom image analysis algorithms. We believe that the work required to make a clinically applicable prototype can be reduced by making the software extensible, so that researchers can develop their own modules or improvements. Such an initiative might then serve as a bridge between image analysis research and cardiovascular research. The aim of this article is therefore to present the design and validation of a cardiovascular image analysis software package (Segment) and to announce its release in a source code format.

          Results

          Segment can be used for image analysis in magnetic resonance imaging (MRI), computed tomography (CT), single photon emission computed tomography (SPECT) and positron emission tomography (PET). Some of its main features include loading of DICOM images from all major scanner vendors, simultaneous display of multiple image stacks and plane intersections, automated segmentation of the left ventricle, quantification of MRI flow, tools for manual and general object segmentation, quantitative regional wall motion analysis, myocardial viability analysis and image fusion tools. Here we present an overview of the validation results and validation procedures for the functionality of the software. We describe a technique to ensure continued accuracy and validity of the software by implementing and using a test script that tests the functionality of the software and validates the output. The software has been made freely available for research purposes in a source code format on the project home page http://segment.heiberg.se.

          Conclusions

          Segment is a well-validated comprehensive software package for cardiovascular image analysis. It is freely available for research purposes provided that relevant original research publications related to the software are cited.

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          Most cited references17

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          Measurement of the distribution volume of gadopentetate dimeglumine at echo-planar MR imaging to quantify myocardial infarction: comparison with 99mTc-DTPA autoradiography in rats.

          To measure the fractional distribution volume of gadopentetate dimeglumine in normal and reperfused infarcted myocardium at magnetic resonance (MR) imaging by using the fractional distribution volume of technetium 99m-diethylenetriaminepentaacetic acid (DTPA) as an independent reference. Rats were subjected to 1 hour of coronary artery occlusion and 1 hour of reperfusion before inversion-recovery echo-planar imaging or autoradiography. Regional change in relaxation rate (delta R1) ratios for myocardium over blood were compared with radioactivity ratios for myocardium over blood after the injection of 99mTc-DTPA. Both delta R1 and radioactivity ratios demonstrated equilibrium distribution and hence represent partition coefficients (lambda). The fractional distribution volumes were greater in infarcted myocardium (0.90 +/- 0.05 for gadopentetate dimeglumine and 0.89 +/- 0.04 for 99mTc-DTPA) than in normal myocardium (0.23 +/- 0.02 for gadopentetate dimeglumine and 0.16 +/- 0.01 for 99mTc-DTPA). Area at risk at autoradiography was not significantly different from that at histomorphometry. The infarction size defined by using triphenyltetrazolium chloride was 13% +/- 4 smaller than that defined by using autoradiography. The fractional distribution volumes of gadopentetate dimeglumine and 99mTc-DTPA are similar and indicate extracellular distribution in normal myocardium and intracellular as well as extracellular distribution in reperfused infarction. Because the failure of cells to exclude these agents is indicative of necrosis, contrast medium-enhanced MR imaging may be useful to quantify myocardial infarction.
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            Automated quantification of myocardial infarction from MR images by accounting for partial volume effects: animal, phantom, and human study.

            Ethics committees approved human and animal study components; informed written consent was provided (prospective human study [20 men; mean age, 62 years]) or waived (retrospective human study [16 men, four women; mean age, 59 years]). The purpose of this study was to prospectively evaluate a clinically applicable method, accounting for the partial volume effect, to automatically quantify myocardial infarction from delayed contrast material-enhanced magnetic resonance images. Pixels were weighted according to signal intensity to calculate infarct fraction for each pixel. Mean bias +/- variability (or standard deviation), expressed as percentage left ventricular myocardium (%LVM), were -0.3 +/- 1.3 (animals), -1.2 +/- 1.7 (phantoms), and 0.3 +/- 2.7 (patients), respectively. Algorithm had lower variability than dichotomous approach (2.7 vs 7.7 %LVM, P < .01) and did not differ from interobserver variability for bias (P = .31) or variability (P = .38). The weighted approach provides automatic quantification of myocardial infarction with higher accuracy and lower variability than a dichotomous algorithm. (c) RSNA, 2007.
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              Baseline correction of phase contrast images improves quantification of blood flow in the great vessels.

              Phase-contrast Cardiovascular Magnetic Resonance Imaging (CMR) generally requires the analysis of stationary tissue adjacent to a blood vessel to serve as a baseline reference for zero velocity. However, for the heart and great vessels, there is often no stationary tissue immediately adjacent to the vessel. Consequently, uncorrected velocity offsets may introduce substantial errors in flow quantification. The purpose of this study was to assess the magnitude of these flow errors and to validate a clinically applicable method for their correction. In 10 normal volunteers, phase-contrast CMR was used to quantify blood flow in the main pulmonary artery (Qp) and the aorta (Qs). Following image acquisition, phase contrast CMR was performed on a stationary phantom using identical acquisition parameters so as to provide a baseline reference for zero velocity. Aortic and pulmonary blood flow was then corrected using the offset values from the phantom. The mean difference between pulmonary and aortic flow was 26 +/- 21 mL before correction and 7.1 +/- 6.6 mL after correction (p = 0.002). The measured Qp/Qs was 1.25 +/- 0.20 before correction and 1.05 +/- 0.07 after correction (p = 0.001). Phase-contrast CMR can have substantial errors in great vessel flow quantification if there is no correction for velocity offset errors. The proposed method of correction is clinically applicable and provides a more accurate measurement of blood flow.
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                Author and article information

                Journal
                BMC Med Imaging
                BMC Medical Imaging
                BioMed Central
                1471-2342
                2010
                11 January 2010
                : 10
                : 1
                Affiliations
                [1 ]Department of Clinical Physiology, Lund University and Lund University Hospital, Lund, Sweden
                Article
                1471-2342-10-1
                10.1186/1471-2342-10-1
                2822815
                20064248
                6b3356b6-d1f8-47a8-a9b8-6f58e1ac9eb6
                Copyright ©2010 Heiberg et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 September 2009
                : 11 January 2010
                Categories
                Software

                Radiology & Imaging
                Radiology & Imaging

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