Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning

Zhu, Chenglu; Wang, Xiaoyan; Chen, Shengyong; Zhang, Teng; Bai, Cong; Huang, Xiaojie; Xia, Ming; Shao, Zhanpeng; Gu, Zhongwei; Sun, Peiliang

doi:10.1007/s11517-022-02622-z

Cited by 3 publications

(4 citation statements)

References 33 publications

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“…The authors limited the scans by a 128 × 128 × 16 bounding box placed in the center of the ground-truth segmentations. Zhu et al [39] and Alblas et al [28] applied 3D U-Net for CA localization and 2D CNN for CA segmentation method similar to ours that employs the sequential application of two deep learning models for 1) 3D CA centerline detection and 2) 2D CA wall contouring. They achieved a median DSC of 81.3% for the vessel wall on the opensource dataset acquired with the same MR protocol.…”

Section: Discussionmentioning

confidence: 99%

“…They achieved a median DSC of 81.3% for the vessel wall on the opensource dataset acquired with the same MR protocol. Zhu et al [39] achieved DSCs of 89.68% and 80.29% for the lumen and wall segmentation, respectively. They combined deep learning and graph-based approaches and applied them to the multi-sequence MRI acquired with the same protocols.…”

Section: Discussionmentioning

confidence: 99%

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UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

Lavrova¹,

Salahuddin

Woodruff

et al. 2023

IEEE Access

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We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves stateof-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

Lavrova¹,

Salahuddin

Woodruff

et al. 2023

IEEE Access

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“…Currently, ischaemic stroke due to carotid atherosclerotic plaque is the most common cause of death after heart disease and cancer, with some studies suggesting that 80% of cerebral ischaemic processes are caused by carotid atherosclerotic vulnerable plaque 6,7 .Therefore, early detection and identi cation of vulnerable plaque in the carotid arteries is very important.Currently, ultrasound (US), computed tomography angiography (CTA) and digital subtraction angiography (DSA), magnetic resonance imaging (MRI), and other imaging methods are used to evaluate carotid atherosclerotic lesions. Magnetic Resonance Imaging (MRI) and others [8][9][10][11][12] .Among them, ultrasound imaging, with its wide range of applications and high detection rate of vulnerable plaques, is more effective in identifying atherosclerotic plaques in practice than other imaging modalities .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based carotid plaque ultrasound image detection and classification study

Zhang,

Zhao

2024

Preprint

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Objective:To detect and classify carotid plaque ultrasound images based on different deep learning models of convolutional neural networks, and to compare the advantages and disadvantages of each model, with a view to providing a fast and accurate detection method for carotid atherosclerotic plaque ultrasound screening in stroke risk groups using artificial intelligence techniques. Methods:A total of 5611 carotid ultrasound images of 3683 patients from the ultrasound departments of the Eighth People's Hospital of Shanghai, Fengxian District Central Hospital of Shanghai, the Second People's Hospital of Guangdong Province in Guangdong Province, and the People's Hospital of Huainan City in Anhui Province during the period of 17 September 2020 to 17 December 2022 were selected for the study.All carotid ultrasound image data redundant information was cropped, and two attending physicians with more than ten years of experience in cardiovascular ultrasound labelled and classified all the images for diagnosis. The total dataset was randomly split into a training set (3927 images) and a test set (1684 images) in a ratio of 7:3. Four deep learning models-YOLO V7 (ResNet 50) model, YOLO V7 (Inception V3) model, Faster RCNN (ResNet 50) model, and Faster RCNN (Inception V3) model-were used to detect and analyse the carotid artery plaque ultrasound images and to atherosclerotic plaques to identify and classify whether the carotid arteries are vulnerable plaques or stable plaques.The efficacy of the four deep learning models in classifying carotid atherosclerotic plaques was assessed using Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), F1 scores, and Area under the curve of the working characteristics of the subjects (AUC), with P< 0.05 was taken as statistically significant difference. Results:In this study, Faster RCNN model and YOLO V7 network base model were constructed using deep learning algorithms and two different feature extraction networks (ResNet 50 and Inception V3) were used to classify ultrasound images of carotid artery plaques.The Faster RCNN (ResNet 50) model in the test set had ACC, SEN, SPE, AUC were 0.88, 0.94, 0.71, and 0.91, respectively, which was the highest prediction efficacy for carotid atherosclerotic plaque classification among the four models. This study demonstrates the feasibility of deep learning for carotid plaque ultrasound image detection and classification, in which the Faster RCNN (ResNet 50) model has high accuracy and reliability. Conclusion:In the diagnosis of carotid artery vulnerable plaque, the confidence level of the diagnosis using the deep learning Faster RCNN (ResNet 50) model is close to that of intermediate physicians, and the model can improve the diagnosis level of junior ultrasonographers, and also help clinics to formulate a more reasonable ischemic stroke prediction and early warning plan.

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“…Over the past few decades, various medical image segmentation algorithm have been presented, which can be broadly grouped into thresholding ( Jain and Singh, 2022 ; Rawas and El-Zaart, 2022 ), watershed ( Mohanapriya and Kalaavathi, 2019 ; Sadegh et al, 2022 ), clustering ( Xu et al, 2022 ; Zhou et al, 2022 ), conditional random field ( Sun et al, 2020 ; Li et al, 2022 ), dictionary learning ( Yang Y Y et al, 2020 ; Tang et al, 2021 ), graph cut ( Gamechi et al, 2021 ; Zhu et al, 2021 ), region growing ( Rundo et al, 2016 ; Biratu et al, 2021 ), active contour ( Dake et al, 2019 ; Shahvaran et al, 2021 ), quantum-inspired computing ( Sergioli et al, 2021 ; Amin et al, 2022 ), computational intelligence ( Vijay et al, 2016 ; Zhang et al, 2022 ). These traditional methods rely on developers to design algorithms for specific applications.…”

Section: Introductionmentioning

confidence: 99%

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Zhang,

Cheng,

Zhou

et al. 2023

Front. Physiol.

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Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People’s Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).

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Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on flow line learning

Cited by 3 publications

References 33 publications

UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

UR-CarA-Net: A Cascaded Framework With Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

Deep learning-based carotid plaque ultrasound image detection and classification study

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

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