Your browser doesn't support javascript.
loading
Wavelet-based U-shape network for bioabsorbable vascular stents segmentation in IVOCT images.
Lin, Mingfeng; Lan, Quan; Huang, Chenxi; Yang, Bin; Yu, Yuexin.
Afiliación
  • Lin M; Henan Key Laboratory of Cardiac Remodeling and Transplantation, Zhengzhou Seventh People's Hospital, Zhengzhou, China.
  • Lan Q; School of Informatics, Xiamen University, Xiamen, China.
  • Huang C; Department of Neurology and Department of Neuroscience, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
  • Yang B; Fujian Key Laboratory of Brain Tumors Diagnosis and Precision Treatment, Xiamen, China.
  • Yu Y; School of Informatics, Xiamen University, Xiamen, China.
Front Physiol ; 15: 1454835, 2024.
Article en En | MEDLINE | ID: mdl-39210969
ABSTRACT
Background and

Objective:

Coronary artery disease remains a leading cause of mortality among individuals with cardiovascular conditions. The therapeutic use of bioresorbable vascular scaffolds (BVSs) through stent implantation is common, yet the effectiveness of current BVS segmentation techniques from Intravascular Optical Coherence Tomography (IVOCT) images is inadequate.

Methods:

This paper introduces an enhanced segmentation approach using a novel Wavelet-based U-shape network to address these challenges. We developed a Wavelet-based U-shape network that incorporates an Attention Gate (AG) and an Atrous Multi-scale Field Module (AMFM), designed to enhance the segmentation accuracy by improving the differentiation between the stent struts and the surrounding tissue. A unique wavelet fusion module mitigates the semantic gaps between different feature map branches, facilitating more effective feature integration.

Results:

Extensive experiments demonstrate that our model surpasses existing techniques in key metrics such as Dice coefficient, accuracy, sensitivity, and Intersection over Union (IoU), achieving scores of 85.10%, 99.77%, 86.93%, and 73.81%, respectively. The integration of AG, AMFM, and the fusion module played a crucial role in achieving these outcomes, indicating a significant enhancement in capturing detailed contextual information.

Conclusion:

The introduction of the Wavelet-based U-shape network marks a substantial improvement in the segmentation of BVSs in IVOCT images, suggesting potential benefits for clinical practices in coronary artery disease treatment. This approach may also be applicable to other intricate medical imaging segmentation tasks, indicating a broad scope for future research.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Physiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Front Physiol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza