Your browser doesn't support javascript.
loading
A dual-modal dynamic contour-based method for cervical vascular ultrasound image instance segmentation.
Chang, Chenkai; Qi, Fei; Xu, Chang; Shen, Yiwei; Li, Qingwu.
Afiliación
  • Chang C; College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China.
  • Qi F; Changzhou No.2 People's Hospital, No.29 Xinglong Lane, Changzhou 213004, Jiangsu, China.
  • Xu C; College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China.
  • Shen Y; College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China.
  • Li Q; College of Information Science and Engineering, Hohai University, Changzhou 213200, Jiangsu, China.
Math Biosci Eng ; 21(1): 1038-1057, 2024 Jan.
Article en En | MEDLINE | ID: mdl-38303453
ABSTRACT

OBJECTIVES:

We intend to develop a dual-modal dynamic contour-based instance segmentation method that is based on carotid artery and jugular vein ultrasound and its optical flow image, then we evaluate its performance in comparison with the classic single-modal deep learning networks.

METHOD:

We collected 2432 carotid artery and jugular vein ultrasound images and divided them into training, validation and test dataset by the ratio of 811. We then used these ultrasound images to generate optical flow images with clearly defined contours. We also proposed a dual-stream information fusion module to fuse complementary features between different levels extracted from ultrasound and optical flow images. In addition, we proposed a learnable contour initialization method that eliminated the need for manual design of the initial contour, facilitating the rapid regression of nodes on the contour to the ground truth points.

RESULTS:

We verified our method by using a self-built dataset of carotid artery and jugular vein ultrasound images. The quantitative metrics demonstrated a bounding box detection mean average precision of 0.814 and a mask segmentation mean average precision of 0.842. Qualitative analysis of our results showed that our method achieved smoother segmentation boundaries for blood vessels.

CONCLUSIONS:

The dual-modal network we proposed effectively utilizes the complementary features of ultrasound and optical flow images. Compared to traditional single-modal instance segmentation methods, our approach more accurately segments the carotid artery and jugular vein in ultrasound images, demonstrating its potential for reliable and precise medical image analysis.
Asunto(s)
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 Asunto principal: Procesamiento de Imagen Asistido por Computador / Arterias Carótidas Tipo de estudio: Clinical_trials / Diagnostic_studies / Qualitative_research Idioma: En Revista: Math Biosci Eng Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Arterias Carótidas Tipo de estudio: Clinical_trials / Diagnostic_studies / Qualitative_research Idioma: En Revista: Math Biosci Eng Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos