A method for giant aneurysm segmentation using Euler’s elastica

Chen, Yu; Courbebaisse, Guy; Yu, Jianjiang; Lu, Dongxiang; Ge, Fei

doi:10.1016/j.bspc.2020.102111

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Machine learning-based aneurysm segmentation methods focus on segmenting the cerebrovascular structure images and then applying this information to the segmentation of intracranial aneurysm images. For example, segmentation of intracranial aneurysm images acquired using 3D Rotational Angiography and computed tomography angiography (CTA) based on a geometric deformation model [ 10 ], a geodesic active contour (GAC) combined with Euler’s elastic model for segmenting CTA-acquired large aneurysm images (size, > 25 mm) [ 11 ], and automatic segmentation of aneurysm images using the improved threshold-based level set method [ 12 ] have been proposed.…”

Section: Introductionmentioning

confidence: 99%

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images

Yuan

Peng

Guo

et al. 2022

Vis. Comput. Ind. Biomed. Art

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Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences. The U-Net model serves as a backbone, combining dense block and convolution block attention module (CBAM). The dense block is composed of a batch normalization layer, an randomly rectified linear unit activation function, and a convolutional layer, for mitigation of vanishing gradients, for multiplexing of aneurysm features, and for improving the network training efficiency. The CBAM is composed of a channel attention module and a spatial attention module, improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information. Owing to the large variation of aneurysm sizes, multi-scale fusion is performed during up-sampling, for adaptive segmentation of MRA-acquired aneurysm images. The model was tested on the MICCAI 2020 ADAM dataset, and its generalizability was validated on the clinical aneurysm dataset (aneurysm sizes: < 3 mm, 3–7 mm, and > 7 mm) supplied by the Affiliated Hospital of Qingdao University. A good clinical application segmentation performance was demonstrated.

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Section: Introductionmentioning

confidence: 99%

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images

Yuan

Peng

Guo

et al. 2022

Vis. Comput. Ind. Biomed. Art

View full text Add to dashboard Cite

show abstract

“…Kai et al (Lawonn et al 2019) present a graph-cut based method for aneurysm segmentation. Yu et al (Chen et al 2020) present a geodesic active contour (GAC) and Euler's elastica model-based method for aneurysm segmentation, where GAC segments the giant aneurysms in high contrast regions and the elastica model estimates the missing boundaries in low contrast regions. Thus, the numerous automatic segmentation algorithms reported so far in the literature have potential limitations that warrant further research on automatic methods.…”

Section: Related Workmentioning

confidence: 99%

Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage

Regaya

Amira

Dakua

2023

Netw Model Anal Health Inform Bioinforma

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Image segmentation being the first step is always crucial for brain aneurysm treatment planning; it is also crucial during the procedure. A robust brain aneurysm segmentation has the potential to prevent the blood leakage, also known as sentinel hemorrhage. Here, we present a method combining a multiresolution and a statistical approach in two dimensional domain to segment cerebral aneurysm in which the Contourlet transform (CT) extracts the image features, while the Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. The proposed algorithm is tested on Three-Dimensional Rotational Angiography (3DRA) datasets; the average values of segmentation accuracy, DSC, FPR, FNR, specificity, and sensitivity, are found to be 99.72%, 93.52%, 0.07%, 5.23%, 94.77%, and 99.96%, respectively.

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StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm

Irfan

Malik

Ahmad

et al. 2023

Computerized Medical Imaging and Graphics

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A method for giant aneurysm segmentation using Euler’s elastica

Cited by 6 publications

References 25 publications

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images

DCAU-Net: dense convolutional attention U-Net for segmentation of intracranial aneurysm images

Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage

StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm

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