NAG-Net: Nested attention-guided learning for segmentation of carotid lumen-intima interface and media-adventitia interface

Cardiovascular diseases (CVD), as the leading cause of death in the world, poses a serious threat to human health. The segmentation of carotid Lumen-intima interface (LII) and Media-adventitia interface (MAI) is a prerequisite for measuring intima-media thickness (IMT), which is of great significanc...

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Veröffentlicht in:	Computers in biology and medicine Jg. 156; S. 106718
Hauptverfasser:	Huang, Qinghua, Zhao, Liangrun, Ren, Guanqing, Wang, Xiaoyi, Liu, Chunying, Wang, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.04.2023 Elsevier Limited
Schlagworte:	Accuracy Adventitia - diagnostic imaging Artificial intelligence Asymmetric encoder–decoder architecture Attention Attention mechanism Biology Cardiovascular Diseases Carotid arteries Carotid Arteries - diagnostic imaging Carotid Intima-Media Thickness Coders Contours Deep learning Feature extraction Humans Image Processing, Computer-Assisted - methods Internal Medicine LII and MAI segmentation Medical image segmentation Methods Other Segmentation Thickness measurement Transfer learning Tunica Intima - diagnostic imaging Ultrasonic imaging Veins & arteries Visual perception Attention mechanism Asymmetric encoder–decoder architecture LII and MAI segmentation Medical image segmentation
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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Zusammenfassung:	Cardiovascular diseases (CVD), as the leading cause of death in the world, poses a serious threat to human health. The segmentation of carotid Lumen-intima interface (LII) and Media-adventitia interface (MAI) is a prerequisite for measuring intima-media thickness (IMT), which is of great significance for early screening and prevention of CVD. Despite recent advances, existing methods still fail to incorporate task-related clinical domain knowledge and require complex post-processing steps to obtain fine contours of LII and MAI. In this paper, a nested attention-guided deep learning model (named NAG-Net) is proposed for accurate segmentation of LII and MAI. The NAG-Net consists of two nested sub-networks, the Intima-Media Region Segmentation Network (IMRSN) and the LII and MAI Segmentation Network (LII-MAISN). It innovatively incorporates task-related clinical domain knowledge through the visual attention map generated by IMRSN, enabling LII-MAISN to focus more on the clinician’s visual focus region under the same task during segmentation. Moreover, the segmentation results can directly obtain fine contours of LII and MAI through simple refinement without complicated post-processing steps. To further improve the feature extraction ability of the model and reduce the impact of data scarcity, the strategy of transfer learning is also adopted to apply the pretrained weights of VGG-16. In addition, a channel attention-based encoder feature fusion block (EFFB-ATT) is specially designed to achieve efficient representation of useful features extracted by two parallel encoders in LII-MAISN. Extensive experimental results have demonstrated that our proposed NAG-Net outperformed other state-of-the-art methods and achieved the highest performance on all evaluation metrics. •We Propose a novel nested DCNN model for accurate segmentation of LII and MAI.•Task-related clinical domain knowledge was incorporated as segmentation prior.•An attention-based module is designed to efficiently use the prior knowledge.•Transfer learning strategy is used to further improve the model performance.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0010-4825 1879-0534 1879-0534
DOI:	10.1016/j.compbiomed.2023.106718