Left ventricle segmentation via two-layer level sets with circular shape constraint

Yang, Cong; Wu, Weiguo; Su, Yuanqi; Zhang, Shaoxiang

doi:10.1016/j.mri.2017.01.011

Cited by 31 publications

(20 citation statements)

References 29 publications

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“…In the traditional level set methods [ 12 , 14 , 15 ], the level set function is reinitialized after each time step for smooth transitions during the curve evolution process. In [ 13 ], an energy penalization term is introduced by Liu et al, which maintains the level set function as a signed distance function (SDF) to remove the need of reinitialization.…”

Section: The Proposed Methodsmentioning

confidence: 99%

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Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours

Soomro

Akram

Munir

et al. 2017

Computational and Mathematical Methods in Medicine

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Segmentation of left and right ventricles plays a crucial role in quantitatively analyzing the global and regional information in the cardiac magnetic resonance imaging (MRI). In MRI, the intensity inhomogeneity and weak or blurred object boundaries are the problems, which makes it difficult for the intensity-based segmentation methods to properly delineate the regions of interests (ROI). In this paper, a hybrid signed pressure force function (SPF) is proposed, which yields both local and global image fitted differences in an additive fashion. A characteristic term is also introduced in the SPF function to restrict the contour within the ROI. The overlapping dice index and Hausdorff-Distance metrics have been used over cardiac datasets for quantitative validation. Using 2009 LV MICCAI validation dataset, the proposed method yields DSC values of 0.95 and 0.97 for endocardial and epicardial contours, respectively. Using 2012 RV MICCAI dataset, for the endocardial region, the proposed method yields DSC values of 0.97 and 0.90 and HD values of 8.51 and 7.67 for ED and ES, respectively. For the epicardial region, it yields DSC values of 0.92 and 0.91 and HD values of 6.47 and 9.34 for ED and ES, respectively. Results show its robustness in the segmentation application of the cardiac MRI.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…Recently, Liu e al. [ 13 ] and Yang et al [ 14 ] proposed active contour segmentation methods for cardiac MRI data. These methods segment endocardial and epicardial boundaries by using level set formulation.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours

Soomro

Akram

Munir

et al. 2017

Computational and Mathematical Methods in Medicine

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“…In recent years, segmentation and quantification of the LV from cardiac MRI images have received much attention to diagnose cardiovascular disease. Many studies have proposed semi-automatic segmentation methods to delineate the LV borders, such as active contour [ 17 , 18 ], level set [ 19 , 20 , 21 ], graph cut [ 22 ], dynamic programming, and atlas-based models. These traditional segmentation methods necessitate user intervention, which is a time-consuming and tedious task.…”

Section: Related Workmentioning

confidence: 99%

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network

et al. 2022

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Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.

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“…Most of them involve the computation of regional and global features, from which it is possible to detect cardiac dysfunction. Among these approaches, the deformable models algorithms and their variants have been widely used in cardiac MRI [8][9][10]. Nandagopalan and al.…”

Section: Introductionmentioning

confidence: 99%

“…Most of them involve the computation of regional and global features, from which it is possible to detect cardiac dysfunction. Among these approaches, the deformable models' algorithms and their variants have been widely used in CMRI [8–10]. Nandagopalan et al proposed a new approach combining the K‐mean clustering algorithm and active contour model to detect cardiac borders [11].…”

Section: Introductionmentioning

confidence: 99%

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

Mahmoudi

Ameur

Ammari

et al. 2020

IET Image Processing

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Magnetic Resonance Imaging (MRI) has emerged as the golden reference for cardiac examination. This modality allows the assessment of human cardiovascular morphology, functioning, and perfusion. Although a couple of challenging issues, such as the cardiac MR image's features and the large variability of images among several patients, still influences the cardiac cavities' segmentation and needs to be carried out. In this paper, we have profoundly reviewed and fully compared semi-automated segmentation methods performed on cardiac Cine-MR short-axis images for the evaluation of the left ventricular functions. However, the number of parameters handled by the synthesized works is limited if not null. For the sake of ensuring the highest coverage of the LV parameters computing, we have introduced a parallel watershedbased approach to segment the left ventricular allowing hence the computation of six parameters (End-Diastolic Volume, End-Systolic Volume, Ejection Fraction, Cardiac output, Stroke Volume and Left Ventricular Mass). An algorithm is associated with main considered measurements. The experimental results that were obtained through studying twenty patients' MRI data base, demonstrate the accuracy of our approach for estimating real values of the maximal set of parameters thanks to a faithful segmentation of the myocardium.

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Left ventricle segmentation via two-layer level sets with circular shape constraint

Cited by 31 publications

References 29 publications

Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours

Segmentation of Left and Right Ventricles in Cardiac MRI Using Active Contours

Automatic Left Ventricle Segmentation from Short-Axis Cardiac MRI Images Based on Fully Convolutional Neural Network

Left ventricular segmentation based on a parallel watershed transformation towards an accurate heart function evaluation

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