AI in EE

AI IN DIVISIONS

AI in Circuit Division

AI in EE

AI IN DIVISIONS

AI in Circuit Division ​

AI in Circuit Division

Seok-Hwan Oh, Myeong-Gee Kim, Youngmin Kim, Hyuksool Kwon, Hyeon-Min Bae, “A Neural Framework for Multi-Variable Lesion Quantification Through B-mode Style Transfer”, International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), Sept. 2021.

Abstract: In this paper, we present a scalable lesion-quantifying neural network based on b-mode-to-quantitative neural style transfer. Quantitative tissue characteristics have great potential in diagnostic ultrasound since pathological changes cause variations in biomechanical properties. The proposed system provides four clinically critical quantitative tissue images such as sound speed, attenuation coefficient, effective scatterer diameter, and effective scatterer concentration simultaneously by applying quantitative style information to structurally accurate b-mode images. The proposed system was evaluated through numerical simulation, and phantom and ex-vivo measurements. The numerical simulation shows that the proposed framework outperforms the baseline model as well as existing state-of-the-art methods while achieving significant parameter reduction per quantitative variables. In phantom and ex-vivo studies, the BQI-Net demonstrates that the proposed system achieves sufficient sensitivity and specificity in identifying and classifying cancerous lesions.