Automatic cardiac ventricle segmentation in MR images: a validation study

Grosgeorge, Damien; Petitjean, Caroline; Caudron, Jérôme; Fares, Jeannette; Dacher, Jean‐Nicolas

doi:10.1007/s11548-010-0532-6

Cited by 61 publications

(39 citation statements)

References 42 publications

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“…However, cardiac MRI remains time consuming for RV function assessment, as reported in previously published studies [14][15][16]. Indeed, postprocessing software solutions are less efficient for RV automatic segmentation than for LV, and RV segmentation remains mostly manual, even though improvement in postprocessing has recently been reported [17]. Therefore, the Qt method is rarely performed in daily practice, and RV functional assessment is mostly visually performed except in cases of specific indication, such as in patients with CHD, or in the context of research studies.…”

Section: Introductionmentioning

confidence: 96%

Diagnostic accuracy and variability of three semi-quantitative methods for assessing right ventricular systolic function from cardiac MRI in patients with acquired heart disease

et al. 2011

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Section: Introductionmentioning

confidence: 96%

Diagnostic accuracy and variability of three semi-quantitative methods for assessing right ventricular systolic function from cardiac MRI in patients with acquired heart disease

et al. 2011

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“…To the best of our knowledge, most of the segmentation algorithms in the literature use a circular or elliptic shape in the center of the slice as the initial contour for the LV [3,4,6]. Nevertheless, these assumptions will fail specially in case of abnormality and muscle deficiency.…”

Section: Initial Contour Extractionmentioning

confidence: 99%

“…The Left Ventricle (LV) segmentation meets lots of challenges due to the huge variation in size and shape of the LV due to the slice position as well as the heart size variability, the motion artifacts as well as the effect of papillary muscles. The RV segmentation is considered more challenging than the LV as it suffers from the complex crescent structure, wall irregularities, inhomogeneity and its ill-defined borders [3].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images

Mousa

Zayed

Yassine

2016

IJCT

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I S S N 2277-3061 V o l u m e 1 5 N u m b e r 1 1 I n t e r n a t i o n a l J o u r n a l o f C o m p u t e r s & T e c h n o l o g y 7 2 1 8 | P a g e c o u n c i l f o r I n n o v a t i v e R e s e a r c h A u g u s t 2 0 1 6 w w w . c i r w o r l d . ABSTRACTCardiovascular diseases (CVDs) cause 31% of the death rate globally. Automatic accurate segmentation is needed for CVDs early detection. In this paper, we study the effect of the registration and initialization of the level set segmentation on the performance of extracting the heart ventricles for the short axis cardiac perfusion MRI images, as a result, we propose a modified workflow to automatically segment the ventricles by mitigating the levelset initial contour extraction in order to improve the segmentation results accuracy. In the registration experiments, the translational transformation was studied based on both the spatial and frequency domain. The frequency domain based registration is mainly established based on the phase correlation methodology. As for the segmentation experiments, the level set initialization was done through extracting the ventricles' real shape from each slice. Though, the final contour of any frame will be used as the initial contour for the next frame. The second initialization strategy was based on defining the initial contour for each frame using the polar representation of the image. Two short axis view datasets of cardiac magnetic resonance (CMR) perfusion imaging were used in testing the proposed methods. Dice coefficient, sensitivity, specificity and Hausdorff distance have been used to evaluate and validate the segmentation results. The system workflow consists of five main modules: preprocessing, localization, initial contour extraction, registration, and segmentation. The segmentation accuracy for left and right ventricles improved from 72% to 77% and from 70% to 81% using the spatial domain based registration algorithm. The polar-based initialization strategy improves the segmentation accuracy from 77% to 81% and from 81% to 82% for the left and right ventricles respectively.

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“…A number of methods have been worked on using active contour model for left ventricle segmentation. Grosgeorge et al (5) utilized wellknown region based active contour approach, Chan-Vese approach, for segmentation of both left and right ventricle. Their results show a satisfying segmentation, but because of using region term solely, this method results good only in homogenous regions with well-defined borders.…”

Section: Introductionmentioning

confidence: 99%

Left Ventricle Segmentation Using a Combination of Region Growing and Graph Based Method

Oghli

Mohammadzadeh

et al. 2017

Iran J Radiol

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Background: Left ventricle segmentation plays an essential role in computation of cardiac functional parameters such as ventricular end diastolic and end systolic volumes, ejection fraction, myocardial mass, and wall thickness and also wall motion analysis. Manual segmentation is also time consuming and suffers from inter and intra observer variability. Several approaches have been proposed that segment the left ventricle (LV) by automatic and semi-automatic methods, but the problem is still open due to the huge shape variety of the left ventricle and motion artifact.

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Automatic cardiac ventricle segmentation in MR images: a validation study

Abstract: Ventricular segmentation based on region-driven active contours provided satisfactory results in MRI, without the use of a priori knowledge. The study of error distribution allows identification of potential improvements in algorithm performance.

Cited by 61 publications

References 42 publications

Diagnostic accuracy and variability of three semi-quantitative methods for assessing right ventricular systolic function from cardiac MRI in patients with acquired heart disease

Diagnostic accuracy and variability of three semi-quantitative methods for assessing right ventricular systolic function from cardiac MRI in patients with acquired heart disease

Factors Affecting the Segmentation of the Heart Ventricles in Short Axis Cardiac Perfusion MRI Images

Left Ventricle Segmentation Using a Combination of Region Growing and Graph Based Method

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