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Deep learning-based key point detection algorithm assisting vessel centerline extraction
Xiqian Zhang1,2, Wanqing Sun3, Hui Zhang4
1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Insights
A new key point detection algorithm improves vessel centerline extraction accuracy for plaque analysis. This method enhances precision in tortuous vessels and significantly reduces processing time.
Area of Science:
- Medical Imaging
- Computational Anatomy
- Vascular Biology
Background:
- Vessel centerline extraction is crucial for quantitative plaque analysis.
- Current methods struggle with tortuous vessels, leading to inaccurate results.
- This study introduces a key point detection algorithm to improve accuracy.
Purpose of the Study:
- To develop and evaluate a key point detection algorithm for enhancing vessel centerline extraction.
- To improve the accuracy of quantitative plaque analysis in cerebrovascular diseases.
- To address limitations of existing algorithms in handling complex vessel geometries.
Main Methods:
- Retrospective analysis of 539 patients with cerebrovascular disease using 3.0-T MRI.
- Selection of 32 critical key points (e.g., carotid siphon, bifurcations).
- Evaluation using undetected points, erroneous points, point accuracy, and average centerline distance (ACD).
Main Results:
- The algorithm achieved an average accuracy of 88.99% for 32 key points, exceeding 90% for 18 points.
- High accuracy (97%) was observed in sharp bends of the carotid siphon.
- Average centerline distance (ACD) improved from 0.529±0.334 mm to 0.484±0.321 mm; detection time reduced from ~320s to ~2s.
Conclusions:
- The proposed algorithm automatically and accurately detects key points, especially in the internal carotid and middle cerebral arteries.
- This enhances vessel centerline extraction accuracy, aiding plaque assessment.
- The algorithm offers a significant improvement for quantitative analysis of cerebrovascular plaque.
Background:
Vessel centerline extraction assists in the quantitative analysis of plaque. Current algorithms generate significant errors for tortuous vessels, leading to inaccurate centerline extraction. This study proposed a key point detection algorithm to assist in vessel centerline extraction for the further quantitative analysis of plaque.
Methods:
A total of 539 patients with cerebrovascular disease from multiple centers were enrolled in this retrospective study. All the patients underwent 3.0-T magnetic resonance imaging (MRI) scans. Based on the experimental experience of radiologists and clinical requirements, 32 key points were chosen, including the carotid siphon, tiny vessels, and vessel bifurcations. Accurate point detection can improve the accuracy of centerline detection. The evaluation indices included the number of undetected points (undetected_num), the number of erroneously detected points (errodetected_num), and the accuracy of each point (pointacc). The average centerline distance (ACD) was used to evaluate the improvement in centerline extraction.
Results:
The average accuracy of the algorithm in detecting of the 32 points was 88.99%, and the algorithm had an accuracy exceeding 90% for 18 of these points. The accuracy of the algorithm at the sharp bend of the carotid siphon section reached 97%. The accuracy of the algorithm in detecting the points in the internal carotid artery and middle cerebral artery was 95.4%. Using the key point detection algorithm, the ACD for the right carotid artery was reduced to 0.484±0.321 mm but was 0.529±0.334 mm without the key point detection algorithm. The time required to detect the 32 key points was reduced from 319.843±6.434 to 2.046±0.315 seconds when the algorithm was used.
Conclusions:
The proposed algorithm was able to automatically and accurately detect the 32 key points, especially those in the internal carotid artery and middle cerebral artery, improving vessel centerline extraction accuracy, and thus assisting in plaque assessment.
