SPASM: A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data

Assen, H.C. van; Danilouchkine, Mikhail G.; Frangi, Alejandro F.; Ordas, S.; Westenberg, Jos J.M.; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.

doi:10.1016/j.media.2005.12.001

Cited by 190 publications

(113 citation statements)

References 30 publications

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“…A 4D probabilistic atlas of the heart and 3D intensity template was used in Lorenzo-Valdes et al [19] to localize the LV. Many other methods have been proposed that segment the LV from short-axis images [20][21][22], multiple views [23,24], and using registration information [25,26]. Paragios [21] uses prior shape information in an active contour framework for segmenting the LV.…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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“…Note also that SA initialization was robust to slices misregistration, since possible initialization inaccuracies were compensated by the rigid deformation. Producing a mean positioning error inferior to one pixel for both SA and LA images, comparable with inter-expert drawing variability [11], the method proved accurate and robust. We validated the method for ED phase only, since the segmentation of the whole cardiac cycle can be then obtained using automatic contour propagation [22], shown to preserve its accuracy within acceptable ranges while being much faster than phase-by-phase segmentation.…”

Section: Validationmentioning

confidence: 89%

“…Many publications propose automatic or semi-automatic methods for segmenting the LV in SA CMR images [4][5][6][7][8] or in multiple views [9][10][11], to mention a few. Since SA CMR images, acquired over multiple breath-holds, are often misregistered due to patient motion or inconsistent respiration, 3D segmentation and analysis methods require a registration preprocessing step [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Segmentation of Cine Cardiac MR Images

Fradkin

Ciofolo

Mory

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. A typical Cardiac Magnetic Resonance (CMR) examination includes acquisition of a sequence of short-axis (SA) and long-axis (LA) images covering the cardiac cycle. Quantitative analysis of the heart function requires segmentation of the left ventricle (LV) SA images, while segmented LA views allow more accurate estimation of the basal slice and can be used for slice registration. Since manual segmentation of CMR images is very tedious and time-consuming, its automation is highly required. In this paper, we propose a fully automatic 2D method for segmenting LV consecutively in LA and SA images. The approach was validated on 35 patients giving mean segmentation error smaller than one pixel, both for LA and SA, and accurate LV volume measurements.

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“…A majority of techniques described in the literature in this context are based on the use of image intensity gradients, and thus may not necessarily be ideally suited for CMR images, because gradients in these images may not be strong enough to allow accurate endocardial border detection and because the algorithms may be dependent on image quality and the specific pulse sequence used for imaging (23). There are different types of algorithms for boundary detection from CMR images, including approaches based on deformable models (24,25), active shape models (26,27), active appearance models (28,29), and expectation maximization method (30). However, these methods usually require extensive manual tracing for building the model database or for the definition of the training set, thus limiting their clinical application.…”

Section: Discussionmentioning

confidence: 99%

Automated frame‐by‐frame endocardial border detection from cardiac magnetic resonance images for quantitative assessment of left ventricular function: Validation and clinical feasibility

Corsi

Veronesi

Lamberti

et al. 2009

Magnetic Resonance Imaging

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Purpose: To develop a technique based on image noise distribution for automated endocardial border detection from cardiac magnetic resonance (CMR) images throughout the cardiac cycle, validate it, and test its clinical utility. Materials and Methods:Images obtained in 36 patients were analyzed using custom software to obtain left ventricular (LV) volume throughout the cardiac cycle, end-systolic and end-diastolic LV volumes, and ejection fraction (EF). Validation against manually-traced endocardial boundaries included intertechnique comparisons of LV volumes, slice areas, and border positions. Then, the clinical feasibility of the dynamic automated analysis of LV function was tested in 14 patients with normal LV function, 12 patients with systolic dysfunction, and 10 patients with diastolic dysfunction.Results: Analysis time for one cardiac cycle was Ͻ15 minutes. Intertechnique comparisons resulted in high correlation (r Ͼ 0.96), small biases (volumes: -6 mL; EF: 4.6%) and narrow limits of agreement (volumes: 17.6 mL; EF: 9.2%). We found significant intergroup differences in multiple quantitative indices of systolic and diastolic function. Conclusion:Fast, automated, dynamic detection of LV endocardial boundaries is feasible and allows accurate quantification of LV size and function, which is potentially clinically useful for objective assessment of systolic and diastolic dysfunction. CARDIAC MAGNETIC RESONANCE (CMR) is a noninvasive imaging modality with excellent spatial and contrast resolution that has become the standard reference in the assessment of left ventricular (LV) size and function (1,2), against which other techniques are frequently validated (3-12). However, this technique relies on the detection of endocardial boundaries, which requires frame-by-frame manual tracing on multiple slices, and is thus of limited value in clinical practice. Moreover, continuous measurement of LV volume requires slice-by-slice, phase-by-phase detection of the endocardial boundaries, which with the commercially available analysis software packages, based on the analysis of image intensity gradients, requires manual corrections in most patients and thus remains semiautomated at best, time consuming, and subjective. Thus, the dynamic nature of CMR imaging is largely unutilized in the clinical assessment of LV function, which is commonly reduced to visually identifying in a small number of slices the end-systolic (ES) and end-diastolic (ED) frames as those that depict the smallest and largest cross-sectional LV cavity areas, respectively. These frames are then traced slice-by-slice and used to obtain ES and ED volumes (ESV and EDV) and calculate the ejection fraction (EF). This practical solution can be challenging in patients with severely reduced systolic function, when volume changes are minimal, and inaccurate in hearts, wherein ventricular contraction is uneven and dyssynchronized so that minimum and maximum cross-sectional areas may appear in different slices at different times. Availability of a reliable technique f...

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SPASM: A 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data

Cited by 190 publications

References 30 publications

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Comprehensive Segmentation of Cine Cardiac MR Images

Automated frame‐by‐frame endocardial border detection from cardiac magnetic resonance images for quantitative assessment of left ventricular function: Validation and clinical feasibility

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