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. 2024 Mar 22;7(1):6.
doi: 10.1186/s42492-024-00157-8.

PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography

Linyuan Wang #  1 , Xiaofeng Zhang #  2 , Congyu Tian  3 , Shu Chen  4 , Yongzhi Deng  5 , Xiangyun Liao  6 , Qiong Wang  3 , Weixin Si  3
Affiliations

PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography

Linyuan Wang et al. Vis Comput Ind Biomed Art. .

Abstract

Cardiovascular disease, primarily caused by atherosclerotic plaque formation, is a significant health concern. The early detection of these plaques is crucial for targeted therapies and reducing the risk of cardiovascular diseases. This study presents PlaqueNet, a solution for segmenting coronary artery plaques from coronary computed tomography angiography (CCTA) images. For feature extraction, the advanced residual net module was utilized, which integrates a deepwise residual optimization module into network branches, enhances feature extraction capabilities, avoiding information loss, and addresses gradient issues during training. To improve segmentation accuracy, a depthwise atrous spatial pyramid pooling based on bicubic efficient channel attention (DASPP-BICECA) module is introduced. The BICECA component amplifies the local feature sensitivity, whereas the DASPP component expands the network's information-gathering scope, resulting in elevated segmentation accuracy. Additionally, BINet, a module for joint network loss evaluation, is proposed. It optimizes the segmentation model without affecting the segmentation results. When combined with the DASPP-BICECA module, BINet enhances overall efficiency. The CCTA segmentation algorithm proposed in this study outperformed the other three comparative algorithms, achieving an intersection over Union of 87.37%, Dice of 93.26%, accuracy of 93.12%, mean intersection over Union of 93.68%, mean Dice of 96.63%, and mean pixel accuracy value of 96.55%.

Keywords: Attention mechanism; Coronary artery plaques; Deep residual networks; Medical image segmentation.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
AResNet network module
Fig. 2
Fig. 2
Network output prediction using the DASPP-BICECA module
Fig. 3
Fig. 3
Loss of BINet network joint assessment model
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Algorithm 1 Joint evaluation of cross-entropy loss function
Fig. 4
Fig. 4
Comparison of four segmentation algorithms, each represented by a row, and the columns display the segmentation result plots. The first row corresponds to the FCN algorithm, which exhibited noticeable segmentation gaps. The second row represents the Deeplabv3 algorithm, which shown a significant over-segmentation in its results. The third row showcases the Deeplabv3plus algorithm, which demonstrates a lower degree of over-segmentation. The fourth row presents the PlaqueNet algorithm proposed in this study
Fig. 5
Fig. 5
Analysis of segmentation evaluation metrics for algorithms
Fig. 6
Fig. 6
Joint assessment of the impact of network loss on segmentation performance
See this image and copyright information in PMC

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