Neural Network-Based Quality Control Method for Breast Full Volume Ultrasound Images
As a non-invasive,radiation-free breast examination technology,automated breast ultra sound(ABUS)has played an increasingly important role in the early screening and diagnosis of breast diseases.However,ABUS images are susceptible to a variety of interference factors during acquisition and processing,such as equipment noise,patient respiratory movement,tissue interface reflection,etc.,which lead to artifacts in the image and reduce the image quality.At the same time,accurate identification of the nipple position is also crucial for the precise positioning and diagnosis of breast diseases.This paper proposes a deep learning-based breast full-volume ultrasound data image quality control method.Using the YOLO target detection network,the detection of interference artifacts and the precise detection of the nipple position are realized respectively.Through experimental verifications,this method performs well in improving the efficiency and accuracy of ABUS image quality control.
automated breast ultra sounddeep learningartifact detectionnipple detectionYOLO