Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery

Jodas, Danilo Samuel; Pereira, Aledir Silveira; Tavares, João Manuel R. S.

doi:10.1007/978-3-319-68195-5_10

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Over the past years, several studies have used computer-aided diagnosis to improve the efficiency and accuracy of segmentation and reduce the burden on doctors for the interpretation of medical images ( Ladak et al, 1999 ; Sakellarios et al, 2012 ; Jodas et al, 2018 ). However, these methods sometimes require user intervention.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Xiong

et al. 2022

Front. Neurosci.

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PurposeTo develop and evaluate an automatic segmentation method of arterial vessel walls and plaques, which is beneficial for facilitating the arterial morphological quantification in magnetic resonance vessel wall imaging (MRVWI).MethodsMRVWI images acquired from 124 patients with atherosclerotic plaques were included. A convolutional neural network-based deep learning model, namely VWISegNet, was used to extract the features from MRVWI images and calculate the category of each pixel to facilitate the segmentation of vessel wall. Two-dimensional (2D) cross-sectional slices reconstructed from all plaques and 7 main arterial segments of 115 patients were used to build and optimize the deep learning model. The model performance was evaluated on the remaining nine-patient test set using the Dice similarity coefficient (DSC) and average surface distance (ASD).ResultsThe proposed automatic segmentation method demonstrated satisfactory agreement with the manual method, with DSCs of 93.8% for lumen contours and 86.0% for outer wall contours, which were higher than those obtained from the traditional U-Net, Attention U-Net, and Inception U-Net on the same nine-subject test set. And all the ASD values were less than 0.198 mm. The Bland–Altman plots and scatter plots also showed that there was a good agreement between the methods. All intraclass correlation coefficient values between the automatic method and manual method were greater than 0.780, and greater than that between two manual reads.ConclusionThe proposed deep learning-based automatic segmentation method achieved good consistency with the manual methods in the segmentation of arterial vessel wall and plaque and is even more accurate than manual results, hence improved the convenience of arterial morphological quantification.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Xiong

et al. 2022

Front. Neurosci.

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“…This approach is fully automatic, because seeds are selected from experimentally determined intervals according to the local histogram, and good agreement with expert manual measurements was achieved. Additionally, Tavares et al (13)(14)(15)(16)(17)(18)(19) proposed a series of novel methods related to automatic artery segmentation, the classification of calcified regions, and hemodynamics in multi-modality medical images.…”

Section: Introductionmentioning

confidence: 99%

Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes

Zhou¹,

Tan²,

Pan³

et al. 2021

Quant Imaging Med Surg

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Background: The objectives of this study were to develop a 3D convolutional deep learning framework (CarotidNet) for fully automatic segmentation of carotid bifurcations in computed tomography angiography (CTA) images and to facilitate the quantification of carotid stenosis and risk assessment of stroke.Methods: Our pipeline was a two-stage cascade network that included a localization phase and a segmentation phase. The network framework was based on the 3D version of U-Net, but was refined in three ways: (I) by adding residual connections and a deep supervision strategy to cope with the vanishing problem in back-propagation; (II) by adopting dilated convolution in order to strengthen the capacity to capture contextual information; and (III) by establishing a hybrid objective function to address the extreme imbalance between foreground and background voxels.Results: We trained our networks on 15 cases and evaluated their performance based on 41 cases from the MICCAI Challenge 2009 dataset. A Dice similarity coefficient of 82.3% was achieved for the test cases. Conclusions:We developed a carotid segmentation method based on U-Net that can segment tiny carotid bifurcation lumens from very large backgrounds with no manual intervention. This was the first attempt to use deep learning to achieve carotid bifurcation segmentation in 3D CTA images. Our results indicate that deep learning is a promising method for automatically extracting carotid bifurcation lumens.

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“…Computer-aided methods have been developed for better identification of the carotid lumen and atherosclerotic components. Automatic segmentation of the lumen of the carotid artery is done in ultrasound Bmode images 33 and in magnetic resonance images 34 . Patient-specific 3D luminal surface reconstruction and computer fluid stimulation gives better understanding of the hemodynamic parameters in the place of the plaque.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Unstable Carotid Plaques – New Image Modalities

et al. 2019

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Ischemic stroke is a socially significant health problem due to high mortality and disability. One of the leading causes for cerebrovascular accidents is the carotid atherosclerosis. The mechanism of its formation presents not only lipid accumulation in the arterial wall but a complex inflammatory disease. The aims of this review are to point the new methods and approaches for diagnostic of the unstable and high-risk carotid plaques. The old plaque imaging modalities emphasized mainly to the degrees of luminal stenosis. The new possibilities reveal plaque morphology so detailed even compared to histological verification. Recent techniques as Shear wave elastography, optical coherence tomography, Superb microvascular imaging, USPIO MRI give information about the pathological mechanisms of carotid atherosclerosis. The efforts are directed to predict the atherosclerotic burden, plaque instability and the occurrence of cerebrovascular events for each patient and to optimize personal management.

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Automatic Segmentation of the Lumen in Magnetic Resonance Images of the Carotid Artery

Cited by 3 publications

References 14 publications

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images

Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes

Characteristics of Unstable Carotid Plaques – New Image Modalities

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