Abstract
BackgroundCommercially 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.ResultsSegment 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.ConclusionsSegment 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.
Highlights
Available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies
Segment is a full-featured software tool for cardiovascular image analysis and to date, it has been used in a wide range of publications ranging from technical algorithm descriptions [3,4,5] to applied research on the effects of cardiac gene therapy [6], perfusion magnetic resonance imaging (MRI) [7], perfusion multidetector computed tomography (CT) [8], applied human physiology [9], validation of an imaging technique in clinical cardiology [10], analysis of infarction with MRI [11] and MDCT [12], and analysis of microinfarction [7], regional cardiac function [13], for the first time quantitatively determine the infarct evolution in man [14], brain imaging [15] and experimental imaging in rodents [16]
The software has been developed with a view to its use in cardiovascular magnetic resonance imaging (MRI) and myocardial perfusion single-photon emission computed tomography (SPECT), but in principle it can be used for image analysis in any organ system, and it has been used for image analysis in computed tomography (CT) and positron emission tomography (PET)
Summary
Available software for cardiovascular image analysis often has limited functionality and frequently lacks the careful validation that is required for clinical studies. 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 serve as a bridge between image analysis research and cardiovascular research. In a recent study it was possible to classify 72 regional myocardial sectors according to the neighbouring sectors and to track them in 22 patients over 5 points in time, generating over 50,000 classified data points [2] This task would have been impossible without advanced scripting capabilities in the analysis software
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