Automatic segmentation of myocardium at risk from contrast enhanced SSFP CMR: validation against expert readers and SPECT.
ABSTRACT: BACKGROUND:Efficacy of reperfusion therapy can be assessed as myocardial salvage index (MSI) by determining the size of myocardium at risk (MaR) and myocardial infarction (MI), (MSI?=?1-MI/MaR). Cardiovascular magnetic resonance (CMR) can be used to assess MI by late gadolinium enhancement (LGE) and MaR by either T2-weighted imaging or contrast enhanced SSFP (CE-SSFP). Automatic segmentation algorithms have been developed and validated for MI by LGE as well as for MaR by T2-weighted imaging. There are, however, no algorithms available for CE-SSFP. Therefore, the aim of this study was to develop and validate automatic segmentation of MaR in CE-SSFP. METHODS:The automatic algorithm applies surface coil intensity correction and classifies myocardial intensities by Expectation Maximization to define a MaR region based on a priori regional criteria, and infarct region from LGE. Automatic segmentation was validated against manual delineation by expert readers in 183 patients with reperfused acute MI from two multi-center randomized clinical trials (RCT) (CHILL-MI and MITOCARE) and against myocardial perfusion SPECT in an additional set (n?=?16). Endocardial and epicardial borders were manually delineated at end-diastole and end-systole. Manual delineation of MaR was used as reference and inter-observer variability was assessed for both manual delineation and automatic segmentation of MaR in a subset of patients (n?=?15). MaR was expressed as percent of left ventricular mass (%LVM) and analyzed by bias (mean?±?standard deviation). Regional agreement was analyzed by Dice Similarity Coefficient (DSC) (mean?±?standard deviation). RESULTS:MaR assessed by manual and automatic segmentation were 36?±?10% and 37?±?11%LVM respectively with bias 1?±?6%LVM and regional agreement DSC 0.85?±?0.08 (n?=?183). MaR assessed by SPECT and CE-SSFP automatic segmentation were 27?±?10%LVM and 29?±?7%LVM respectively with bias 2?±?7%LVM. Inter-observer variability was 0?±?3%LVM for manual delineation and -1?±?2%LVM for automatic segmentation. CONCLUSIONS:Automatic segmentation of MaR in CE-SSFP was validated against manual delineation in multi-center, multi-vendor studies with low bias and high regional agreement. Bias and variability was similar to inter-observer variability of manual delineation and inter-observer variability was decreased by automatic segmentation. Thus, the proposed automatic segmentation can be used to reduce subjectivity in quantification of MaR in RCT. CLINICAL TRIAL REGISTRATION:NCT01379261. NCT01374321.
Project description:<h4>Introduction</h4>Manual delineation of the left ventricle is clinical standard for quantification of cardiovascular magnetic resonance images despite being time consuming and observer dependent. Previous automatic methods generally do not account for one major contributor to stroke volume, the long-axis motion. Therefore, the aim of this study was to develop and validate an automatic algorithm for time-resolved segmentation covering the whole left ventricle, including basal slices affected by long-axis motion.<h4>Methods</h4>Ninety subjects imaged with a cine balanced steady state free precession sequence were included in the study (training set n = 40, test set n = 50). Manual delineation was reference standard and second observer analysis was performed in a subset (n = 25). The automatic algorithm uses deformable model with expectation-maximization, followed by automatic removal of papillary muscles and detection of the outflow tract.<h4>Results</h4>The mean differences between automatic segmentation and manual delineation were EDV -11 mL, ESV 1 mL, EF -3%, and LVM 4 g in the test set.<h4>Conclusions</h4>The automatic LV segmentation algorithm reached accuracy comparable to interobserver for manual delineation, thereby bringing automatic segmentation one step closer to clinical routine. The algorithm and all images with manual delineations are available for benchmarking.
Project description:BACKGROUND: Intensity modulated radiotherapy for head and neck cancer necessitates accurate definition of organs at risk (OAR) and clinical target volumes (CTV). This crucial step is time consuming and prone to inter- and intra-observer variations. Automatic segmentation by atlas deformable registration may help to reduce time and variations. We aim to test a new commercial atlas algorithm for automatic segmentation of OAR and CTV in both ideal and clinical conditions. METHODS: The updated Brainlab automatic head and neck atlas segmentation was tested on 20 patients: 10 cN0-stages (ideal population) and 10 unselected N-stages (clinical population). Following manual delineation of OAR and CTV, automatic segmentation of the same set of structures was performed and afterwards manually corrected. Dice Similarity Coefficient (DSC), Average Surface Distance (ASD) and Maximal Surface Distance (MSD) were calculated for "manual to automatic" and "manual to corrected" volumes comparisons. RESULTS: In both groups, automatic segmentation saved about 40% of the corresponding manual segmentation time. This effect was more pronounced for OAR than for CTV. The edition of the automatically obtained contours significantly improved DSC, ASD and MSD. Large distortions of normal anatomy or lack of iodine contrast were the limiting factors. CONCLUSIONS: The updated Brainlab atlas-based automatic segmentation tool for head and neck Cancer patients is timesaving but still necessitates review and corrections by an expert.
Project description:<h4>Aims</h4>To determine the myocardial salvage index, the extent of infarction needs to be related to the myocardium at risk (MaR). Thus, the ability to assess both infarct size and MaR is of central clinical and scientific importance. The aim of the present study was to explore the relationship between T2-weighted cardiac magnetic resonance (CMR) and contrast-enhanced steady-state free precession (CE-SSFP) CMR for the determination of MaR in patients with acute myocardial infarction.<h4>Methods and results</h4>Twenty-one prospectively included patients with first-time ST-elevation myocardial infarction underwent CMR 1 week after primary percutaneous coronary intervention. For the assessment of MaR, T2-weighted images were acquired before and CE-SSFP images were acquired after the injection of a gadolinium-based contrast agent. For the assessment of infarct size, late gadolinium enhancement images were acquired. The MaR by T2-weighted imaging and CE-SSFP was 29 ± 11 and 32 ± 12% of the left ventricle, respectively. Thus, the MaR with T2-weighted imaging was slightly smaller than that by CE-SSFP (-3.0 ± 4.0%; P < 0.01). There was a significant correlation between the two MaR measures (r(2)= 0.89, P < 0.01), independent of the time after contrast agent administration at which the CE-SSFP was commenced (2-8 min).<h4>Conclusion</h4>There is a good agreement between the MaR assessed by T2-weighted imaging and that assessed by CE-SSFP in patients with reperfused acute myocardial infarction 1 week after the acute event. Thus, both methods can be used to determine MaR and myocardial salvage at this point in time.
Project description: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.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.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.Compared to standard manual approaches, the new highly automatic method for estimating myocardial oedema is accurate and straightforward. It has potential as a generic software tool for physicians to use in clinical practice.
Project description:<h4>Background</h4>Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a critical issue in this process. An atlas-based automatic segmentation was developed to improve the delineation efficiency and reduce inter-observer variation. Additionally, automated segmentation using artificial intelligence (AI) has recently become available. In this study, auto-segmentations by atlas- and AI-based models for Organs at Risk (OAR) in patients with prostate and head and neck cancer were performed and delineation accuracies were evaluated.<h4>Methods</h4>Twenty-one patients with prostate cancer and 30 patients with head and neck cancer were evaluated. MIM Maestro was used to apply the atlas-based segmentation. MIM Contour ProtégéAI was used to apply the AI-based segmentation. Three similarity indices, the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance to agreement (MDA), were evaluated and compared with manual delineations. In addition, radiation oncologists visually evaluated the delineation accuracies.<h4>Results</h4>Among patients with prostate cancer, the AI-based model demonstrated higher accuracy than the atlas-based on DSC, HD, and MDA for the bladder and rectum. Upon visual evaluation, some errors were observed in the atlas-based delineations when the boundary between the small bowel or the seminal vesicle and the bladder was unclear. For patients with head and neck cancer, no significant differences were observed between the two models for almost all OARs, except small delineations such as the optic chiasm and optic nerve. The DSC tended to be lower when the HD and the MDA were smaller in small volume delineations.<h4>Conclusions</h4>In terms of efficiency, the processing time for head and neck cancers was much shorter than manual delineation. While quantitative evaluation with AI-based segmentation was significantly more accurate than atlas-based for prostate cancer, there was no significant difference for head and neck cancer. According to the results of visual evaluation, less necessity of manual correction in AI-based segmentation indicates that the segmentation efficiency of AI-based model is higher than that of atlas-based model. The effectiveness of the AI-based model can be expected to improve the segmentation efficiency and to significantly shorten the delineation time.
Project description:PURPOSE:To evaluate the uncertainty of radiomics features from contrast-enhanced breath-hold helical CT scans of non-small cell lung cancer for both manual and semi-automatic segmentation due to intra-observer, inter-observer, and inter-software reliability. METHODS:Three radiation oncologists manually delineated lung tumors twice from 10 CT scans using two software tools (3D-Slicer and MIM Maestro). Additionally, three observers without formal clinical training were instructed to use two semi-automatic segmentation tools, Lesion Sizing Toolkit (LSTK) and GrowCut, to delineate the same tumor volumes. The accuracy of the semi-automatic contours was assessed by comparison with physician manual contours using Dice similarity coefficients and Hausdorff distances. Eighty-three radiomics features were calculated for each delineated tumor contour. Informative features were identified based on their dynamic range and correlation to other features. Feature reliability was then evaluated using intra-class correlation coefficients (ICC). Feature range was used to evaluate the uncertainty of the segmentation methods. RESULTS:From the initial set of 83 features, 40 radiomics features were found to be informative, and these 40 features were used in the subsequent analyses. For both intra-observer and inter-observer reliability, LSTK had higher reliability than GrowCut and the two manual segmentation tools. All observers achieved consistently high ICC values when using LSTK, but the ICC value varied greatly for each observer when using GrowCut and the manual segmentation tools. For inter-software reliability, features were not reproducible across the software tools for either manual or semi-automatic segmentation methods. Additionally, no feature category was found to be more reproducible than another feature category. Feature ranges of LSTK contours were smaller than those of manual contours for all features. CONCLUSION:Radiomics features extracted from LSTK contours were highly reliable across and among observers. With semi-automatic segmentation tools, observers without formal clinical training were comparable to physicians in evaluating tumor segmentation.
Project description:Purpose. To evaluate whether 3T clinical MRI with a small-animal coil and gradient-echo (GE) sequence could be used to characterize long-term left ventricular remodelling (LVR) following nonreperfused myocardial infarction (MI) using semi-automatic segmentation software (SASS) in a rat model. Materials and Methods. 5 healthy rats were used to validate left ventricular mass (LVM) measured by MRI with postmortem values. 5 sham and 7 infarcted rats were scanned at 2 and 4 weeks after surgery to allow for functional and structural analysis of the heart. Measurements included ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), and LVM. Changes in different regions of the heart were quantified using wall thickness analyses. Results. LVM validation in healthy rats demonstrated high correlation between MR and postmortem values. Functional assessment at 4 weeks after MI revealed considerable reduction in EF, increases in ESV, EDV, and LVM, and contractile dysfunction in infarcted and noninfarcted regions. Conclusion. Clinical 3T MRI with a small animal coil and GE sequence generated images in a rat heart with adequate signal-to-noise ratio (SNR) for successful semiautomatic segmentation to accurately and rapidly evaluate long-term LVR after MI.
Project description:<h4>Purpose</h4>Clinical applicability of renal arterial spin labeling (ASL) MRI is hampered because of time consuming and observer dependent post-processing, including manual segmentation of the cortex to obtain cortical renal blood flow (RBF). Machine learning has proven its value in medical image segmentation, including the kidneys. This study presents a fully automatic workflow for renal cortex perfusion quantification by including machine learning-based segmentation.<h4>Methods</h4>Fully automatic workflow was achieved by construction of a cascade of 3 U-nets to replace manual segmentation in ASL quantification. All 1.5T ASL-MRI data, including M<sub>0</sub> , T<sub>1</sub> , and ASL label-control images, from 10 healthy volunteers was used for training (dataset 1). Trained cascade performance was validated on 4 additional volunteers (dataset 2). Manual segmentations were generated by 2 observers, yielding reference and second observer segmentations. To validate the intended use of the automatic segmentations, manual and automatic RBF values in mL/min/100 g were compared.<h4>Results</h4>Good agreement was found between automatic and manual segmentations on dataset 1 (dice score = 0.78 ± 0.04), which was in line with inter-observer variability (dice score = 0.77 ± 0.02). Good agreement was confirmed on dataset 2 (dice score = 0.75 ± 0.03). Moreover, similar cortical RBF was obtained with automatic or manual segmentations, on average and at subject level; with 211 ± 31 mL/min/100 g and 208 ± 31 mL/min/100 g (P < .05), respectively, with narrow limits of agreement at -11 and 4.6 mL/min/100 g. RBF accuracy with automated segmentations was confirmed on dataset 2.<h4>Conclusion</h4>Our proposed method automates ASL quantification without compromising RBF accuracy. With quick processing and without observer dependence, renal ASL-MRI is more attractive for clinical application as well as for longitudinal and multi-center studies.
Project description:BACKGROUND:Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) using magnitude inversion recovery (IR) or phase sensitive inversion recovery (PSIR) has become clinical standard for assessment of myocardial infarction (MI). However, there is no clinical standard for quantification of MI even though multiple methods have been proposed. Simple thresholds have yielded varying results and advanced algorithms have only been validated in single center studies. Therefore, the aim of this study was to develop an automatic algorithm for MI quantification in IR and PSIR LGE images and to validate the new algorithm experimentally and compare it to expert delineations in multi-center, multi-vendor patient data. METHODS:The new automatic algorithm, EWA (Expectation Maximization, weighted intensity, a priori information), was implemented using an intensity threshold by Expectation Maximization (EM) and a weighted summation to account for partial volume effects. The EWA algorithm was validated in-vivo against triphenyltetrazolium-chloride (TTC) staining (n?=?7 pigs with paired IR and PSIR images) and against ex-vivo high resolution T1-weighted images (n?=?23 IR and n?=?13 PSIR images). The EWA algorithm was also compared to expert delineation in 124 patients from multi-center, multi-vendor clinical trials 2-6 days following first time ST-elevation myocardial infarction (STEMI) treated with percutaneous coronary intervention (PCI) (n?=?124 IR and n?=?49 PSIR images). RESULTS:Infarct size by the EWA algorithm in vivo in pigs showed a bias to ex-vivo TTC of -1?±?4%LVM (R?=?0.84) in IR and -2?±?3%LVM (R?=?0.92) in PSIR images and a bias to ex-vivo T1-weighted images of 0?±?4%LVM (R?=?0.94) in IR and 0?±?5%LVM (R?=?0.79) in PSIR images. In multi-center patient studies, infarct size by the EWA algorithm showed a bias to expert delineation of -2?±?6 %LVM (R?=?0.81) in IR images (n?=?124) and 0?±?5%LVM (R?=?0.89) in PSIR images (n?=?49). CONCLUSIONS:The EWA algorithm was validated experimentally and in patient data with a low bias in both IR and PSIR LGE images. Thus, the use of EM and a weighted intensity as in the EWA algorithm, may serve as a clinical standard for the quantification of myocardial infarction in LGE CMR images. CLINICAL TRIAL REGISTRATION:CHILL-MI: NCT01379261 . MITOCARE:NCT01374321 .
Project description:OBJECTIVES:Assessment of thoracic aortic dimensions with non-ECG-triggered contrast-enhanced magnetic resonance angiography (CE-MRA) is accompanied with motion artefacts and requires gadolinium. To avoid both motion artefacts and gadolinium administration, we evaluated the similarity and reproducibility of dimensions measured on ECG-triggered, balanced steady-state free precession (SSFP) MRA as alternative to CE-MRA. METHODS:All patients, with varying medical conditions, referred for thoracic aortic examination between September 2016 and March 2018, who underwent non-ECG-triggered CE-MRA and SSFP-MRA (1.5 T) were retrospectively included (n = 30). Aortic dimensions were measured after double-oblique multiplanar reconstruction by two observers at nine landmarks predefined by literature guidelines. Image quality was scored at the sinus of Valsalva, mid-ascending aorta and mid-descending aorta by semi-automatically assessing the vessel sharpness. RESULTS:Aortic dimensions showed high agreement between non-ECG-triggered CE-MRA and SSFP-MRA (r = 0.99, p < 0.05) without overestimation or underestimation of aortic dimensions in SSFP-MRA (mean difference, 0.1 mm; limits of agreement, -?1.9 mm and 1.9 mm). Intra- and inter-observer variabilities were significantly smaller with SSFP-MRA for the sinus of Valsalva and sinotubular junction. Image quality of the sinus of Valsalva was significantly better with SSFP-MRA, as fewer images were of impaired quality (3/30) than in CE-MRA (21/30). Reproducibility of dimensions was significantly better in images scored as good quality compared to impaired quality in both sequences. CONCLUSIONS:Thoracic aortic dimensions measured on SSFP-MRA and non-ECG-triggered CE-MRA were similar. As expected, SSFP-MRA showed better reproducibility close to the aortic root because of lesser motion artefacts, making it a feasible non-contrast imaging alternative. KEY POINTS:• SSFP-MRA provides similar dimensions as non-ECG-triggered CE-MRA. • Intra- and inter-observer reproducibilities improve for the sinus of Valsalva and sinotubular junction with SSFP-MRA. • ECG-triggered SSFP-MRA shows better image quality for landmarks close to the aortic root in the absence of cardiac motion.