Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets

Feng, Chaolu; Zhang, Shaoxiang; Zhao, Dazhe; Li, Chunming

doi:10.1118/1.4947126

Cited by 35 publications

(18 citation statements)

References 61 publications

(78 reference statements)

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“…ere are 800 retinal fundus images in the database with annotation for OD and OC given. We apply the proposed DRLSEC method, the multiphase C-V method [37], the distance regularized two-layer level set method (DR2LS) [29], and the proposed model without prior elliptic shape to carry out the segmentation of OD and OC regions. e comparisons between these experimental results are made to evaluate the segmentation performance of these methods.…”

Section: Resultsmentioning

confidence: 99%

“…e DR2LS method is suitable for the extraction of object contour with circular structure. In [29], this method is used for contour extraction of endocardium and epicardium of the left ventricle. e contours of the OD and OC can be seen as a circular structure in fundus images.…”

Section: E Proposed Methodmentioning

confidence: 99%

“…Method. Feng et al [29] proposed a distance regularized two-layer level set (DR2LS) method to extract the inside and outside contours of some circular objects using only one level set function. In this method, the inside and outside contours of the object are represented by the 0-level contour and the k-level contour of the level set function.…”

Section: Distance Regularized Two-layer Level Set Segmentationmentioning

confidence: 99%

“…e data term from some traditional active contour models all can be used in this framework. Feng et al [29] suggested the following data term from the famous region scalable fitting (RSF) model [31] to deal with intensity inhomogeneity of images, which is given by…”

Section: Distance Regularized Two-layer Level Set Segmentationmentioning

confidence: 99%

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Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method

Wang

Chi

et al. 2019

Mathematical Problems in Engineering

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Glaucoma is a group of eye conditions, which can seriously damage optic nerves because of an elevated intraocular pressure. Nowadays, glaucoma has become one of the principal causes of blindness that results in irreversible visual loss. Early screening and treatment of glaucoma can prevent further progression of optic nerve degeneration effectively. e vertical cup-to-disc ratio (CDR) is a commonly used measurement for the detection of glaucoma, and therefore accurate segmentation of optic disc (OD) and optic cup (OC) regions in retinal fundus images is of great significance. In this paper, we present a prior shape constraintbased two-layer level set method for OD and OC segmentation in fundus images. is method uses two different layers of one level set function to represent the OD and OC boundaries. In this method, the distance regularized term is designed to guarantee that the distance between the OD and OC varies smoothly. By introducing the prior shape constraint term energy, the final segmentation results of OD and OC are always in the shape of approximate ellipses. In addition, the proposed method has the property of dealing with the intensity inhomogeneity of fundus images through the local fitting energy embedded. Experiments on images from the Baidu Research database demonstrate that the proposed method has superior performance in terms of accuracy and effectiveness.

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Section: Resultsmentioning

confidence: 99%

Section: E Proposed Methodmentioning

confidence: 99%

Section: Distance Regularized Two-layer Level Set Segmentationmentioning

confidence: 99%

Section: Distance Regularized Two-layer Level Set Segmentationmentioning

confidence: 99%

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Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method

Wang

Chi

et al. 2019

Mathematical Problems in Engineering

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“…In the field of neural computing, the sub-parts are may further used to interpret relationship of the desired objects in the image or tissues for medical images [3,4]. Image segmentation has been studied for decades and many efforts have been devoted in the literature to proposing effective segmentation methods [5,6]. In particular, many methods have been proposed in the field of neural computing in recent years [7,8].…”

Section: Introductionmentioning

confidence: 99%

Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method

2017

Self Cite

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Image segmentation is still an open problem due to the existing of intensity inhomogeneity and noise. To accurately segment images with these biases, a local inhomogeneous intensity clustering (LINC) model is proposed. In LINC, a linear combination of a given set of smooth orthogonal basis functions is used to estimate the bias field. A local clustering criterion function is first defined to cluster the nearly homogeneous intensities in a relatively small neighborhood of each pixel. An energy functional is then defined by integrating the function with respect to the neighborhood center. This energy together with a regularization term and an arc length term are incorporated into a variational level set formulation in which de-nosing is implicitly included due to the implied convolution. Image segmentation and bias correction can be simultaneously achieved by updating variables of the final energy functional iteratively till it is stable or a predetermined iteration number is reached. The proposed model LINC has been extensively tested on both synthetic and real images. Experimental results and comparison with state-of-the-art methods demonstrate the advantages of the proposed model in terms of segmentation accuracy, bias field correction, dealing with noise, and robustness to initialization.

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An iterative multi‐path fully convolutional neural network for automatic cardiac segmentation in cine MR images

Wang³

et al. 2019

Medical Physics

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Purpose Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information may be highly beneficial for segmentation performance improvement. To this end, this paper proposes an iterative multi‐path fully convolutional network (IMFCN) to effectively leverage spatial context for automatic cardiac segmentation in cine MR images. Methods To effectively leverage spatial context information, the proposed IMFCN explicitly models the interslice spatial correlations using a multi‐path late fusion strategy. First, the contextual inputs including both the adjacent slices and the already predicted mask of the above adjacent slice are processed by independent feature‐extraction paths. Then, an atrous spatial pyramid pooling (ASPP) module is employed at the feature fusion process to combine the extracted high‐level contextual features in a more effective way. Finally, deep supervision (DS) and batch‐wise class re‐weighting mechanism are utilized to enhance the training of the proposed network. Results The proposed IMFCN was evaluated and analyzed on the MICCAI 2017 automatic cardiac diagnosis challenge (ACDC) dataset. On the held‐out training dataset reserved for testing, our method effectively improved its counterparts that without spatial context and that with spatial context but using an early fusion strategy. On the 50 subjects test dataset, our method achieved Dice similarity coefficient of 0.935, 0.920, and 0.905, and Hausdorff distance of 7.66, 12.10, and 8.80 mm for LV, RV, and MYO, respectively, which are comparable or even better than the state‐of‐the‐art methods of ACDC Challenge. In addition, to explore the applicability to other datasets, the proposed IMFCN was retrained on the Sunnybrook dataset for LV segmentation and also produced comparable performance to the state‐of‐the‐art methods. Conclusions We have presented an automatic end‐to‐end fully convolutional architecture for accurate cardiac segmentation. The proposed method provides an effective way to leverage spatial context in a two‐dimensional manner and results in precise and consistent segmentation results.

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Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets

Abstract: Experimental results demonstrate the advantages of their method in terms of segmentation accuracy and consistency with the heart anatomy.

Cited by 35 publications

References 61 publications

Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method

Automatic Segmentation of Optic Disc and Cup in Retinal Fundus Images Using Improved Two-Layer Level Set Method

Image segmentation and bias correction using local inhomogeneous iNtensity clustering (LINC): A region-based level set method

An iterative multi‐path fully convolutional neural network for automatic cardiac segmentation in cine MR images

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