Scale Mutualized Perception for Vessel Border Detection in Intravascular Ultrasound Images

Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the h...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2024-07, Vol.21 (4), p.1060-1071
Main Authors: Liu, Xiujian, Feng, Tianyuan, Liu, Weipeng, Song, Liang, Yuan, Yixuan, Hau, William Kongto, Ser, Javier Del, Gao, Zhifan
Format: Article
Language:English
Subjects:
Algorithms
Coronary Artery Disease - diagnostic imaging
Coronary Vessels - diagnostic imaging
Deep Learning
Feature extraction
Humans
Image Interpretation, Computer-Assisted - methods
Interference
intravascular ultrasound
Lumen
Morphology
multi-scale perception
Semantics
Switched mode power supplies
Ultrasonography, Interventional - methods
vessel border detection
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Summary:Vessel border detection in IVUS images is essential for coronary disease diagnosis. It helps to obtain the clinical indices on the inner vessel morphology to indicate the stenosis. However, the existing methods suffer the challenge of scale-dependent interference. Early methods usually rely on the hand-crafted features, thus not robust to this interference. The existing deep learning methods are also ineffective to solve this challenge, because these methods aggregate multi-scale features in the top-down way. This aggregation may bring in interference from the non-adjacent scale. Besides, they only combine the features in all scales, and thus may weaken their complementary information. We propose the scale mutualized perception to solve this challenge by considering the adjacent scales mutually to preserve their complementary information. First, the adjacent small scales contain certain semantics to locate different vessel tissues. Then, they can also perceive the global context to assist the representation of the local context in the adjacent large scale, and vice versa. It helps to distinguish the objects with similar local features. Second, the adjacent large scales provide detailed information to refine the vessel boundaries. The experiments show the effectiveness of our method in 153 IVUS sequences, and its superiority to ten state-of-the-art methods.
ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2022.3224934