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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <url>http://segment.heiberg.se</url>.</p> <p>Conclusions</p> <p>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.</p

Automatic segmentation in CMR - Development and validation of algorithms for left ventricular function, myocardium at risk and myocardial infarction

In this thesis four new algorithms are presented for automatic segmentation in cardiovascular magnetic resonance (CMR); automatic segmentation of the left ventricle, myocardial infarction, and myocardium at risk in two different image types. All four algorithms were implemented in freely available software for image analysis and were validated against reference delineations with a low bias and high regional agreement. CMR is the most accurate and reproducible method for assessment of left ventricular mass and volumes and reference standard for assessment of myocardial infarction. CMR is also validated against single photon emission computed tomography (SPECT) for assessment of myocardium at risk up to one week after acute myocardial infarction. However, the clinical standard for quantification of left ventricular mass and volumes is manual delineation which has been shown to have a large bias between observers from different sites and for myocardium at risk and myocardial infarction there is no clinical standard due to varying results shown for the previously suggested threshold methods. The new automatic algorithms were all based on intensity classification by Expectation Maximization (EM) and incorporation of a priori information specific for each application. Validation was performed in large cohorts of patients with regards to bias in clinical parameters and regional agreement as Dice Similarity Coefficient (DSC). Further, images with reference delineation of the left ventricle were made available for future benchmarking of left ventricular segmentation, and the new automatic algorithms for segmentation of myocardium at risk and myocardial infarction were directly compared to the previously suggested intensity threshold methods. Combining intensity classification by EM with a priori information as in the new automatic algorithms was shown superior to previous methods and specifically to the previously suggested threshold methods for myocardium at risk and myocardial infarction. Added value of using a priori information and intensity correction was shown significant measured by DSC even though not significant for bias. For the previously suggested methods of infarct quantification a poorer result was found in the new multi-center, multi-vendor patient data than in the original validation in animal studies or single center patient studies. Thus, the results in this thesis also show the importance ofusing both bias and DSC for validation and performing validation in images of representative quality as in multi-center, multi-vendor patient studies

Highly automatic quantification of myocardial oedema in patients with acute myocardial infarction using bright blood T2-weighted CMR

&lt;p&gt;Background: T2-weighted cardiovascular magnetic resonance (CMR) is clinically-useful for imaging the ischemic area-at-risk and amount of salvageable myocardium in patients with acute myocardial infarction (MI). However, to date, quantification of oedema is user-defined and potentially subjective.&lt;/p&gt; &lt;p&gt;Methods: We describe a highly automatic framework for quantifying myocardial oedema from bright blood T2-weighted CMR in patients with acute MI. Our approach retains user input (i.e. clinical judgment) to confirm the presence of oedema on an image which is then subjected to an automatic analysis. The new method was tested on 25 consecutive acute MI patients who had a CMR within 48 hours of hospital admission. Left ventricular wall boundaries were delineated automatically by variational level set methods followed by automatic detection of myocardial oedema by fitting a Rayleigh-Gaussian mixture statistical model. These data were compared with results from manual segmentation of the left ventricular wall and oedema, the current standard approach.&lt;/p&gt; &lt;p&gt;Results: The mean perpendicular distances between automatically detected left ventricular boundaries and corresponding manual delineated boundaries were in the range of 1-2 mm. Dice similarity coefficients for agreement (0=no agreement, 1=perfect agreement) between manual delineation and automatic segmentation of the left ventricular wall boundaries and oedema regions were 0.86 and 0.74, respectively.&lt;/p&gt

In vivo contrast free chronic myocardial infarction characterization using diffusion-weighted cardiovascular magnetic resonance.

BackgroundDespite the established role of late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) in characterizing chronic myocardial infarction (MI), a significant portion of chronic MI patients are contraindicative for the use of contrast agents. One promising alternative contrast free technique is diffusion weighted CMR (dwCMR), which has been shown ex vivo to be sensitive to myocardial fibrosis. We used a recently developed in vivo dwCMR in chronic MI pigs to compare apparent diffusion coefficient (ADC) maps with LGE imaging for infarct characterization.MethodsIn eleven mini pigs, chronic MI was induced by complete occlusion of the left anterior descending artery for 150&nbsp;minutes. LGE, cine, and dwCMR imaging was performed 8&nbsp;weeks post MI. ADC maps were derived from three orthogonal diffusion directions (b = 400&nbsp;s/mm2) and one non-diffusion weighted image. Two semi-automatic infarct classification methods, threshold and full width half max (FWHM), were performed in both LGE and ADC maps. Regional wall motion (RWM) analysis was performed and compared to ADC maps to determine if any observed ADC change was significantly influenced by bulk motion.ResultsADC of chronic MI territories was significantly increased (threshold: 2.4 ± 0.3&nbsp;μm2/ms, FWHM: 2.4 ± 0.2&nbsp;μm2/ms) compared to remote myocardium (1.4 ± 0.3&nbsp;μm2/ms). RWM was significantly reduced (threshold: 1.0 ± 0.4&nbsp;mm, FWHM: 0.9 ± 0.4&nbsp;mm) in infarcted regions delineated by ADC compared to remote myocardium (8.3 ± 0.1&nbsp;mm). ADC-derived infarct volume and location had excellent agreement with LGE. Both LGE and ADC were in complete agreement when identifying transmural infarcts. Additionally, ADC was able to detect LGE-delineated infarcted segments with high sensitivity, specificity, PPV, and NPV. (threshold: 0.88, 0.93, 0.87, and 0.94, FWHM: 0.98, 0.97, 0.93, and 0.99, respectively).ConclusionsIn vivo diffusion weighted CMR has potential as a contrast free alternative for LGE in characterizing chronic MI

Volume Tracking: A new method for quantitative assessment and visualization of intracardiac blood flow from three-dimensional, time-resolved, three-component magnetic resonance velocity mapping

<p>Abstract</p> <p>Background</p> <p>Functional and morphological changes of the heart influence blood flow patterns. Therefore, flow patterns may carry diagnostic and prognostic information. Three-dimensional, time-resolved, three-directional phase contrast cardiovascular magnetic resonance (4D PC-CMR) can image flow patterns with unique detail, and using new flow visualization methods may lead to new insights. The aim of this study is to present and validate a novel visualization method with a quantitative potential for blood flow from 4D PC-CMR, called Volume Tracking, and investigate if Volume Tracking complements particle tracing, the most common visualization method used today.</p> <p>Methods</p> <p>Eight healthy volunteers and one patient with a large apical left ventricular aneurysm underwent 4D PC-CMR flow imaging of the whole heart. Volume Tracking and particle tracing visualizations were compared visually side-by-side in a visualization software package. To validate Volume Tracking, the number of particle traces that agreed with the Volume Tracking visualizations was counted and expressed as a percentage of total released particles in mid-diastole and end-diastole respectively. Two independent observers described blood flow patterns in the left ventricle using Volume Tracking visualizations.</p> <p>Results</p> <p>Volume Tracking was feasible in all eight healthy volunteers and in the patient. Visually, Volume Tracking and particle tracing are complementary methods, showing different aspects of the flow. When validated against particle tracing, on average 90.5% and 87.8% of the particles agreed with the Volume Tracking surface in mid-diastole and end-diastole respectively. Inflow patterns in the left ventricle varied between the subjects, with excellent agreement between observers. The left ventricular inflow pattern in the patient differed from the healthy subjects.</p> <p>Conclusion</p> <p>Volume Tracking is a new visualization method for blood flow measured by 4D PC-CMR. Volume Tracking complements and provides incremental information compared to particle tracing that may lead to a better understanding of blood flow and may improve diagnosis and prognosis of cardiovascular diseases.</p

Semi-automatic segmentation of myocardium at risk in T2-weighted cardiovascular magnetic resonance

Background: T2-weighted cardiovascular magnetic resonance (CMR) has been shown to be a promising technique for determination of ischemic myocardium, referred to as myocardium at risk (MaR), after an acute coronary event. Quantification of MaR in T2-weighted CMR has been proposed to be performed by manual delineation or the threshold methods of two standard deviations from remote (2SD), full width half maximum intensity (FWHM) or Otsu. However, manual delineation is subjective and threshold methods have inherent limitations related to threshold definition and lack of a priori information about cardiac anatomy and physiology. Therefore, the aim of this study was to develop an automatic segmentation algorithm for quantification of MaR using anatomical a priori information. Methods: Forty-seven patients with first-time acute ST-elevation myocardial infarction underwent T2-weighted CMR within 1 week after admission. Endocardial and epicardial borders of the left ventricle, as well as the hyper enhanced MaR regions were manually delineated by experienced observers and used as reference method. A new automatic segmentation algorithm, called Segment MaR, defines the MaR region as the continuous region most probable of being MaR, by estimating the intensities of normal myocardium and MaR with an expectation maximization algorithm and restricting the MaR region by an a priori model of the maximal extent for the user defined culprit artery. The segmentation by Segment MaR was compared against inter observer variability of manual delineation and the threshold methods of 2SD, FWHM and Otsu. Results: MaR was 32.9 +/- 10.9% of left ventricular mass (LVM) when assessed by the reference observer and 31.0 +/- 8.8% of LVM assessed by Segment MaR. The bias and correlation was, -1.9 +/- 6.4% of LVM, R = 0.81 (p < 0.001) for Segment MaR, -2.3 +/- 4.9%, R = 0.91 (p < 0.001) for inter observer variability of manual delineation, -7.7 +/- 11.4%, R = 0.38 (p = 0.008) for 2SD, -21.0 +/- 9.9%, R = 0.41 (p = 0.004) for FWHM, and 5.3 +/- 9.6%, R = 0.47 (p < 0.001) for Otsu. Conclusions: There is a good agreement between automatic Segment MaR and manually assessed MaR in T2-weighted CMR. Thus, the proposed algorithm seems to be a promising, objective method for standardized MaR quantification in T2-weighted CMR

Manual correction of semi-automatic three-dimensional echocardiography is needed for right ventricular assessment in adults; validation with cardiac magnetic resonance

Background: Three-dimensional echocardiography (3DE) and semi-automatic right ventricular delineation has been proposed as an appropriate method for right ventricle (RV) evaluation. We aimed to examine how manual correction of semi-automatic delineation influences the accuracy of 3DE for RV volumes and function in a clinical adult setting using cardiac magnetic resonance (CMR) as the reference method. We also examined the feasibility of RV visualization with 3DE. Methods: 62 non-selected patients were examined with 3DE (Sonos 7500 and iE33) and with CMR (1.5T). Endocardial RV contours of 3DE-images were semi-automatically assessed and manually corrected in all patients. End-diastolic (EDV), end-systolic (ESV) volumes, stroke volume (SV) and ejection fraction (EF) were computed. Results: 53 patients (85%) had 3DE-images feasible for examination. Correlation coefficients and Bland Altman biases between 3DE with manual correction and CMR were r = 0.78, -22 +/- 27 mL for EDV, r = 0.83, -7 +/- 16 mL for ESV, r = 0.60, -12 +/- 18 mL for SV and r = 0.60, -2 +/- 8% for EF (p < 0.001 for all r-values). Without manual correction r-values were 0.77, 0.77, 0.70 and 0.49 for EDV, ESV, SV and EF, respectively (p < 0.001 for all r-values) and biases were larger for EDV, SV and EF (-32 +/- 26 mL, -21 +/- 15 mL and -6 +/- 9%, p <= 0.01 for all) compared to manual correction. Conclusion: Manual correction of the 3DE semi-automatic RV delineation decreases the bias and is needed for acceptable clinical accuracy. 3DE is highly feasible for visualizing the RV in an adult clinical setting