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A Neural Framework for Multi-Variable Lesion Quantification Through B-mode Style Transfer

Seok-Hwan Oh1,  Myeong-Gee Kim1,  Young-Min Kim1,  Hyuksool Kwon2,  Hyeon-Min Bae1  
Springer, Medical Image Computing and Computer Assisted Intervention (MICCAI)
  • 1. Department of Electrical Engineering, KAIST, Daejeon, South Korea
  • 2. Department of Emergency Medicine, SNUBH, Seong-nam, South Korea


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.

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