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Published on: December 15, 2023
Complex carotid artery segmentation in multi-contrast MR sequences by improved optimal surface graph cuts based on
Chenglu Zhu1,2, Xiaoyan Wang3, Shengyong Chen4
1School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China.
Insights
This study introduces a novel framework for segmenting carotid artery lumen and wall in MRI scans, improving accuracy for cardiovascular disease assessment with reduced training costs.
Area of Science:
- Medical Imaging
- Cardiovascular Disease Research
- Biomedical Engineering
Background:
- Carotid atherosclerosis is a primary cause of cardiovascular disease and mortality.
- Multi-contrast MRI offers high sensitivity and specificity for identifying atherosclerotic plaque components.
- Accurate segmentation of the diseased carotid artery is crucial for quantitative atherosclerosis evaluation.
Purpose of the Study:
- To develop an integrated segmentation framework for precise lumen and wall segmentation of diseased carotid arteries from MR images.
- To address challenges in segmentation caused by complex plaque morphology and limited annotated data.
- To offer a computationally efficient alternative to existing deep learning methods.
Main Methods:
- Proposed an integrated segmentation framework combining a lightweight prediction model with improved optimal surface graph cuts (OSG).
- Implemented a simplified flow line sampling and post-reconstruction method to decrease graph construction costs.
- Introduced a flexibly adaptive smoothing penalty to preserve the shape of the diseased carotid surface.
Main Results:
- Achieved Dice coefficients of 89.68% for lumen and 80.29% for wall segmentation.
- Obtained average surface distances of 0.2480 mm for lumen and 0.3396 mm for wall.
- Demonstrated precise and reliable segmentation with a significantly small training cost.
Conclusions:
- The proposed framework provides accurate and reliable segmentation of carotid artery lumen and wall in patients with atherosclerosis.
- The method offers a computationally efficient solution for quantitative assessment of carotid atherosclerosis.
- This approach holds promise for improving the evaluation and management of cardiovascular disease.
Abstract:
Carotid atherosclerosis is one of the leading causes of cardiovascular disease with high mortality. Multi-contrast MRI can identify atherosclerotic plaque components with high sensitivity and specificity. Accurate segmentation of the diseased carotid artery from MR images is very essential to quantitatively evaluate the state of atherosclerosis. However, due to the complex morphology of atherosclerosis plaques and the lack of well-annotated data, the segmentation of lumen and wall is very challenging. Different from popular deep learning methods, in this paper, we propose an integration segmentation framework by introducing a lightweight prediction model and improved optimal surface graph cuts (OSG), which adopts a simplified flow line sampling and post-reconstructing method to reduce the cost of graph construction. Moreover, a flexibly adaptive smoothing penalty is presented for maintaining the shape of diseased carotid surface. For the experiments, we have collected an MR image dataset from patients with carotid atherosclerosis and evaluated our method by cross-validation. It can reach 89.68%/80.29% of dice coefficients and 0.2480 mm/0.3396 mm of average surface distances on the lumen/wall segmentation, respectively. The experimental results show that our method can generate precise and reliable segmentation of both lumen and wall of diseased carotid artery with a quite small training cost.
