In-line Automated Tracking for Ventricular Function With Magnetic Resonance Imaging

Li, Bo; Liu, Yingmin; Occleshaw, Christopher J.; Cowan, Brett R.; Young, Alistair A.

doi:10.1016/j.jcmg.2010.04.013

Cited by 55 publications

(52 citation statements)

References 5 publications

(7 reference statements)

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“…It is noted that our result still has a gap with the best published myocardium segmentation result of DM=0.84. This was achieved by the state-of-the-art semi-automatic segmentation method [20] on the SATCOM 2011 Challenge Data [21], in which an expert guide was employed to improve the segmentation results.…”

Section: Testing Resultsmentioning

confidence: 99%

Automatic Segmentation of Left Ventricular Myocardium by Deep Convolutional and De:convolutional Neural Networks

Yang¹,

Gobeawan²,

Yeo³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Section: Testing Resultsmentioning

confidence: 99%

Automatic Segmentation of Left Ventricular Myocardium by Deep Convolutional and De:convolutional Neural Networks

Yang¹,

Gobeawan²,

Yeo³

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…More details on the data and its availability can be found in [17,18]. In the effort to establish a set of baseline segmentations for the image population, all images were segmented semi-automatically using the techniques described in [19]. Throughout this manuscript, we will refer to these semi-automated segmentation masks as the "reference masks" or "ground truth segmentations" and will be used both for training or for assessing the results of the proposed ROI detection method.…”

Section: Cardiac Mri Datamentioning

confidence: 99%

A robust automated left ventricle region of interest localization technique using a cardiac cine MRI atlas

Ben-Zikri

Linte

2016

Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling

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Region of interest detection is a precursor to many medical image processing and analysis applications, including segmentation, registration and other image manipulation techniques. The optimal region of interest is often selected manually, based on empirical knowledge and features of the image dataset. However, if inconsistently identified, the selected region of interest may greatly affect the subsequent image analysis or interpretation steps, in turn leading to incomplete assessment during computer-aided diagnosis or incomplete visualization or identification of the surgical targets, if employed in the context of pre-procedural planning or image-guided interventions. Therefore, the need for robust, accurate and computationally efficient region of interest localization techniques is prevalent in many modern computer-assisted diagnosis and therapy applications. Here we propose a fully automated, robust, a priori learning-based approach that provides reliable estimates of the left and right ventricle features from cine cardiac MR images. The proposed approach leverages the temporal frame-to-frame motion extracted across a range of short axis left ventricle slice images with small training set generated from les than 10% of the population. This approach is based on histogram of oriented gradients features weighted by local intensities to first identify an initial region of interest depicting the left and right ventricles that exhibits the greatest extent of cardiac motion. This region is correlated with the homologous region that belongs to the training dataset that best matches the test image using feature vector correlation techniques. Lastly, the optimal left ventricle region of interest of the test image is identified based on the correlation of known ground truth segmentations associated with the training dataset deemed closest to the test image. The proposed approach was tested on a population of 100 patient datasets and was validated against the ground truth region of interest of the test images manually annotated by experts. This tool successfully identified a mask around the LV and RV and furthermore the minimal region of interest around the LV that fully enclosed the left ventricle from all testing datasets, yielding a 98% overlap with their corresponding ground truth. The achieved mean absolute distance error between the two contours that normalized by the radius of the ground truth is 0.20 ± 0.09.

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“…One particular shape representation is a finite element model, which provides an efficient and accurate representation of complex geometries [11]. This method has been shown to provide a compact and powerful representation of shape and function of the LV, and has been validated against ex-vivo LV mass, against manually-drawn contours in patients with regional wall motion abnormalities, and against cardiac output flow in healthy subjects [12], [13]. However, the statistical analysis of shape parameters has previously been limited by the lack of substantial sample size and/or bias between acquisition protocols.…”

Section: Introductionmentioning

confidence: 99%

Atlas-Based Quantification of Cardiac Remodeling Due to Myocardial Infarction

et al. 2014

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Myocardial infarction leads to changes in the geometry (remodeling) of the left ventricle (LV) of the heart. The degree and type of remodeling provides important diagnostic information for the therapeutic management of ischemic heart disease. In this paper, we present a novel analysis framework for characterizing remodeling after myocardial infarction, using LV shape descriptors derived from atlas-based shape models. Cardiac magnetic resonance images from 300 patients with myocardial infarction and 1991 asymptomatic volunteers were obtained from the Cardiac Atlas Project. Finite element models were customized to the spatio-temporal shape and function of each case using guide-point modeling. Principal component analysis was applied to the shape models to derive modes of shape variation across all cases. A logistic regression analysis was performed to determine the modes of shape variation most associated with myocardial infarction. Goodness of fit results obtained from end-diastolic and end-systolic shapes were compared against the traditional clinical indices of remodeling: end-diastolic volume, end-systolic volume and LV mass. The combination of end-diastolic and end-systolic shape parameter analysis achieved the lowest deviance, Akaike information criterion and Bayesian information criterion, and the highest area under the receiver operating characteristic curve. Therefore, our framework quantitatively characterized remodeling features associated with myocardial infarction, better than current measures. These features enable quantification of the amount of remodeling, the progression of disease over time, and the effect of treatments designed to reverse remodeling effects.

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In-line Automated Tracking for Ventricular Function With Magnetic Resonance Imaging

Cited by 55 publications

References 5 publications

Automatic Segmentation of Left Ventricular Myocardium by Deep Convolutional and De:convolutional Neural Networks

Automatic Segmentation of Left Ventricular Myocardium by Deep Convolutional and De:convolutional Neural Networks

A robust automated left ventricle region of interest localization technique using a cardiac cine MRI atlas

Atlas-Based Quantification of Cardiac Remodeling Due to Myocardial Infarction

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