An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images

Ma, Yurun; Wang, Li; Ma, Yide; Dong, Min; Du, Shiqiang; Sun, Xiaoyan

doi:10.1007/s11548-016-1429-9

Cited by 21 publications

(15 citation statements)

References 39 publications

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“…The GVF snake model can expand the distribution range of the external force field and improves the edge extraction of the “recessed” area to a certain extent. However, its edge extraction of “deep recessed area” still has defects [ 21 ], which may be due to the loss of some detailed information in the image while the GVF snake model expands the range of the external force field. Therefore, an improved method capable of extracting deep recessed areas vortical gradient vector flow (VGVF) snake model is proposed based on GVF snake model.…”

Section: Methodsmentioning

confidence: 99%

Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images

Huang

Zhu

Wang

2021

Journal of Healthcare Engineering

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This paper intends to explore the effect of the enhanced snake variable model in the segmentation of cardiac ultrasound images and its adoption in quantitative measurement of cardiac cavity. First, the basic principles of the traditional snake model and the gradient vector flow (GVF) snake model are explained. Then, an ellipsoid model is constructed to obtain the initial contour of the heart based on the three-dimensional volume of cardiac ultrasound image, and a discretized triangular mesh model is generated. Finally, the vortical gradient vector flow (VGVF) external force field is introduced and combined with the greedy algorithm to process the deformation of the initial ellipsoid contour of the heart. The segmentation effect is quantitatively evaluated regarding the area overlap rate (AOR) and the mean contour distance (MCD). The results show that the VGVF snake model can segment the deep recessed area of the “U-shaped map” in contrast to the traditional snake model and the GVF snake model. After being applied to ultrasonic image segmentation, the VGVF snake model obtains the segmentation result that is close to the doctor’s manual segmentation result, and the average AOR and MCD are 97.4% and 3.2, respectively. The quantitative evaluation of the cardiac cavity is carried out based on the segmentation results, and the measurement of the volume change of the left ventricle within a cardiac cycle is realized. To sum up, VGVF snake model is superior to the traditional snake and GVF snake models in terms of ultrasonic image segmentation, which realizes the three-dimensional segmentation and quantitative calculation of the cardiac cavity.

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Section: Methodsmentioning

confidence: 99%

Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images

Huang

Zhu

Wang

2021

Journal of Healthcare Engineering

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“…Resulting mean overlapping indices with the reference segmentation in normal subjects was 90% for LV endocardium and 92% for LVepicardium, corresponding to mean APDs of 1.7 and 1.8 mm, respectively. Ma et al 23 proposed a neural network approach for gradient-based endocardium extraction coupled with a gradient vector flow snake for epicardium segmentation. In a mixed population of healthy and pathological subjects, the method yielded mean overlapping indices with the reference segmentation of 89% for endocardium and 92% for epicardium, both corresponding to APDs of 2.4 mm.…”

Section: Discussionmentioning

confidence: 99%

“…While most segmentation methods focus on endocardial border delineation for LV volume and function assessment, several algorithms also extend to epicardium segmentation allowing (based on voxel summation and an average value for myocardium density of 1.05 g∕mL) to estimate MM. 17,[21][22][23] The aim of the present paper is to describe a semiautomated algorithm for LV endocardial and epicardial contour delineation in SA cine-MR images, which requires minimal operator intervention. The proposed method is hybrid in the sense that it combines the results of three distinct segmentation algorithms (adaptive thresholding, active contour, and region growing) in order to optimize LV cavity extraction.…”

Section: Introductionmentioning

confidence: 99%

Development and clinical validation of a hybrid method for semiautomated left ventricle endocardial and epicardial boundary extraction on cine-magnetic resonance images

et al. 2018

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We describe a hybrid method for left ventricle (LV) endocardial and epicardial segmentation on cardiac magnetic resonance (CMR) images requiring minimal operator intervention. Endocardium extraction results from the union of three independent estimations based on adaptive thresholding, region growing, and active contour with Chan-Vese energy function. Epicardium segmentation relies on conditional morphological dilation of the endocardial mask followed by active contour optimization. The proposed method was first evaluated using an open access database of 18 CMR for which expert manual contouring was available. The method was further validated on a retrospective cohort of 29 patients, who underwent CMR with expert manual segmentation. Regarding the open access database, similarity (Dice index) between hybrid and expert segmentations was good for end-diastolic (ED) endocardium (0.92), end-systolic (ES) endocardium (0.88), and ED epicardium (0.92). As for derived LV parameters, concordance (Lin's coefficient) was good for ED volume (0.91), ES volume (0.93), ejection fraction (EF; 0.89), and fair for myocardial mass (MM; 0.74). Regarding the retrospective patient study, concordance between expert and hybrid estimations was excellent for ED volume (0.95), ES volume (0.96), good for EF (0.86), and fair for MM (0.71). Hybrid segmentation resulted in small biases ([Formula: see text] for ED volume, [Formula: see text] for ES volume, [Formula: see text] for EF, and [Formula: see text] for MM) with little clinical relevance and acceptable for routine practice. The quickness and robustness of the proposed hybrid method and its ability to provide LV volumes, functions, and masses highly concordant with those given by expert segmentation support its pertinence for routine clinical use.

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“…For endocardium segmentation, we have proposed an SPCNN based method in [22]. Firstly, the ROI is extracted based on the time-domain characteristics of 3D data to decrease the computation and complexity of our algorithm.…”

Section: Endocardiumsegmentation Via Spcnnmentioning

confidence: 99%

A Novel Approach For 3D Surface Reconstruction Of LV Endocardium Based On SPCNN And MC

Lei¹,

Ma²,

Wang³

et al. 2016

Proceedings of the 2016 4th International Conference on Electrical &Amp; Electronics Engineering and Computer Science (ICEEECS

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The auxiliary diagnosis based on three-dimensional (3D) modeling techniques can improve the diagnosis efficiency of heart disease. In this paper, we propose a new method for the 3D modeling of left ventricle. The novel approach incorporatestwo key points: one is LV endocardium segmentation via the simplified pulse-coupled neural network (SPCNN), and the other is interpolation between slices. To ensure the accuracy of modeling, we firstly use the cubic spline interpolation to increase the resolution between slices. Then, the endocardial contours of all slices are automatically obtained based on SPCNN. Finally, Marching Cubes (MC) algorithm is implemented to reconstruct the LV endocardial surface. We test the new method on four setsof 3D data and obtain effective results. The APD and ODM are respectively 1.7950mm and 91.34% for the endocardium segmentation, which makes the surface reconstruction of LV endocardium accurate andsmooth.

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An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images

Abstract: We propose an effective and fast method combing the SPCNN and the improved GVF for the automatic segmentation of LV.

Cited by 21 publications

References 39 publications

Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images

Adoption of Snake Variable Model-Based Method in Segmentation and Quantitative Calculation of Cardiac Ultrasound Medical Images

Development and clinical validation of a hybrid method for semiautomated left ventricle endocardial and epicardial boundary extraction on cine-magnetic resonance images

A Novel Approach For 3D Surface Reconstruction Of LV Endocardium Based On SPCNN And MC

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