Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Niu, Yanmin; Lan, Qing; Wang, Xuchu

doi:10.1016/j.neucom.2019.04.052

Cited by 7 publications

(6 citation statements)

References 47 publications

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“…Atlases have many recent applications in medical image segmentation, involving organs like: lung [1], pancreas [2], prostate [3], bones [4], cartilage [5], brain tissues and lesions [6], [7], [8], [9], cardiac structures (e.g. myocardium) [10], [11], and multiple abdominal organs [12]. Atlases are involved in the processing of image data collected through various imaging modalities, including magnetic resonance imaging (MRI) [3], [6], [7], computed tomography (CT) [1], [4], CT angiography [10], positron emission tomography (PET) [11], X-ray [13] and mammography [14].…”

Section: Introductionmentioning

confidence: 99%

“…myocardium) [10], [11], and multiple abdominal organs [12]. Atlases are involved in the processing of image data collected through various imaging modalities, including magnetic resonance imaging (MRI) [3], [6], [7], computed tomography (CT) [1], [4], CT angiography [10], positron emission tomography (PET) [11], X-ray [13] and mammography [14]. For further information on atlas-based image segmentation techniques, there are available systematic review papers (e.g.…”

Section: Introductionmentioning

confidence: 99%

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The role of atlases and multi-atlases in brain tissue segmentation based on multispectral magnetic resonance image data

et al. 2021

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Atlas assisted image segmentation has been quite popular in medical imaging during the last two decades. The atlas is able to provide prior information on the imaged organ's shape, appearance, and local texture or intensity distribution. In case of segmenting images via pixelwise classification, the final segmentation result is obtained through a fusion of the classification outcome with the local atlas information. In other words, the atlas guides the classifier towards the shape of local structures normally situated at the given location. This paper proposes to demonstrate the advantages a multi-atlas can bring in a segmentation process of the main tissues in infant brain based on multi-spectral MRI records. Three supervised machine learning methods are deployed to segment brain tissues, with and without the use of the atlas. Differences are evaluated using statistical accuracy indicators. Atlases improved the overall segmentation accuracy by 2.5-3.5%, depending on the deployed classifier method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of atlases and multi-atlases in brain tissue segmentation based on multispectral magnetic resonance image data

et al. 2021

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“…Atlases have recently been involved in several medical imaging problems, including the segmentation of brain tissues and lesions [1], [2], [3], prostate [4], lung [5], cardiac structures (e.g. myocardium) [6], [7], pancreas [8], [9], bones [10], cartilage [11], and multiple abdominal organ [12]. Atlases are used in segmentation problems based on image data originating from virtually all imaging modalities, including magnetic resonance images (MRI) [1], [2], [4], computed tomography (CT) [5], [9], [10], CT angiography [6], positron emission tomography (PET) [7], X-ray [13] and mammography [14].…”

Section: Introductionmentioning

confidence: 99%

“…myocardium) [6], [7], pancreas [8], [9], bones [10], cartilage [11], and multiple abdominal organ [12]. Atlases are used in segmentation problems based on image data originating from virtually all imaging modalities, including magnetic resonance images (MRI) [1], [2], [4], computed tomography (CT) [5], [9], [10], CT angiography [6], positron emission tomography (PET) [7], X-ray [13] and mammography [14]. A systematic review of earlier image segmentation solutions based on atlases and multi-atlases is given by Cabezas et al [15].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

Fulop

Győrfi

Suranyi

et al. 2020

2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Atlases are frequently employed to assist medical image segmentation with prior information. This paper introduces a multi-atlas architecture that is trained to locally characterize the appearance (average intensity and standard deviation) of normal tissues in various observed and computed data channels of brain MRI records. The multiple atlas is then deployed to enhance the accuracy of an ensemble learning based brain tumor segmentation procedure that uses binary decision trees. The proposed method is validated using the low-grade tumor volumes of the BraTS 2016 train data set. The use of atlases improve the segmentation quality, causing a rise of up to 1.5% in average Dices scores.

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“…Finally, both images can obtain accurate segmentation results. Niu et.al [24] created a new active contour model for computed tomography angiography images processing. In the algorithm, shape prior and local region fitting terms are employed.…”

Section: Introductionmentioning

confidence: 99%

A Shape Prior-Based Active Contour Model for Automatic Images Segmentation

Jiang

2020

IEEE Access

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Due to the variable shapes of objects, high noise intensity and complex environments, the field of image segmentation still has great challenges. To address these issues, we present a new image segmentation strategy based on active contour model and shape priori information, which can accurately and efficiently segment various images. The data fitting term, inspired by Chan-Vese (C-V) model, is used to guide the curve evolving to desired target boundary. Meanwhile, the contour is utilized to reconstruct a prior shape so that can help to deal with images in the presence of complex target. After that, the length regularity term of energy functional is incorporated to ensure the stable calculation of the evolution curve. The quantitative and qualitative experiments on various real and medical images indicate that our method is more efficient and accurate than the existing unified models.

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Structured graph regularized shape prior and cross-entropy induced active contour model for myocardium segmentation in CTA images

Cited by 7 publications

References 47 publications

The role of atlases and multi-atlases in brain tissue segmentation based on multispectral magnetic resonance image data

The role of atlases and multi-atlases in brain tissue segmentation based on multispectral magnetic resonance image data

Brain Tumor Segmentation from MRI Data Using Ensemble Learning and Multi-Atlas

A Shape Prior-Based Active Contour Model for Automatic Images Segmentation

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