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Published on: December 6, 2024
Vessel membrane segmentation and calcification location in intravascular ultrasound images using a region detector
Yi Huang1, Wenjun Yan1, Menghua Xia1
1Department of Electronic Engineering, Fudan University, Shanghai, 200433, China.
Insights
This study presents an automated method for segmenting vessel membranes and locating calcifications in intravascular ultrasound (IVUS) images. The approach accurately extracts media adventitia (MA)/lumen borders and identifies calcifications, improving atherosclerosis diagnosis.
Area of Science:
- Medical Imaging
- Cardiovascular Technology
- Biomedical Engineering
Background:
- Manual segmentation of media adventitia (MA)/lumen borders and calcification in intravascular ultrasound (IVUS) images is time-consuming.
- Existing automated methods struggle with simultaneous vessel membrane extraction and calcification detection across different IVUS systems.
Purpose of the Study:
- To develop a fully automatic approach for segmenting vessel membranes and locating calcifications in IVUS images.
- To address limitations of existing methods in handling diverse IVUS data and performing simultaneous detection.
Main Methods:
- Utilized extremal regions and a flexible selection strategy for vessel membrane extraction.
- Incorporated acoustic properties for calcification localization.
- Employed polar coordinates for approximate segmentation, followed by smoothing for final results.
Main Results:
- Achieved competitive performance compared to existing methods on public datasets.
- Demonstrated accurate MA/lumen border detection with mean Hausdorff distances of 1.13/1.21 mm and 0.47/0.28 mm for different IVUS frequencies.
- Successfully located calcifications in high-frequency IVUS frames.
Conclusions:
- The proposed strategy effectively extracts MA/lumen borders across various IVUS frames.
- The method accurately locates calcifications in high-frequency IVUS images, aiding atherosclerosis diagnosis.
Background And Objective:
Segmenting vessel membranes and locating the calcific region in intravascular ultrasound (IVUS) images aid physicians in the diagnosis of atherosclerosis. However, the manual extraction of the media adventitia (MA)/lumen border and calcification location are cumbersome due to the excessive number of IVUS frames. Moreover, most existing (semi-)automatic detection methods cannot achieve both vessel membrane extraction and calcification location simultaneously, and they are unable to detect vessel membranes in IVUS frames from different acquisition systems.
Method:
A fully automatic approach is proposed based on extremal regions and a flexible selection strategy to extract vessel membranes in different IVUS frames and locate the calcific region in high-frequency ones. Three main steps are included in the algorithm. First, a region detector is employed to extract extremal regions from an IVUS image. Then, according to the selection strategy, a part of the extracted regions is selected. At the same time, the calcification is located according to its special acoustic properties. Next, approximate MA and lumen border segmentation is achieved based on the selected extremal regions and the located calcification in polar coordinates. Finally, the final segmentation results are obtained by smoothing the approximate values.
Result:
To demonstrate the feasibility of the method, it was evaluated based on a standard public dataset. Furthermore, to quantitatively evaluate the segmentation performance, the Hausdorff distance (HD), Jaccard measure (JM) and percentage of area difference (PAD) were used. The results show that a mean HD of 1.13/1.21 mm, a mean JM of 0.83/0.77 and a mean PAD of 0.11/0.23 are achieved for MA/lumen border detection in 77 40-MHz IVUS images. For MA/lumen border extraction in 435 20-MHz IVUS frames, the average HD, JM and PAD values are 0.47/0.28 mm, 0.84/0.89 and 0.13/0.10, respectively. In addition, the approach successfully achieves calcification location in 40-MHz IVUS frames. In comparison with other published methods, the method proposed in this study is competitive.
Conclusion:
According to these results, our strategy can extract MA/lumen borders in different IVUS frames and effectively locate calcification in high-frequency IVUS frames.
