TY - JOUR
T1 - DefCor-Net
T2 - Physics-aware ultrasound deformation correction
AU - Jiang, Zhongliang
AU - Zhou, Yue
AU - Cao, Dongliang
AU - Navab, Nassir
N1 - Publisher Copyright:
© 2023
PY - 2023/12
Y1 - 2023/12
N2 - The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis, particularly in the emerging field of computer-assisted diagnosis. This article presents a novel physics-aware deformation correction approach based on a coarse-to-fine, multi-scale deep neural network (DefCor-Net). To achieve pixel-wise performance, DefCor-Net incorporates biomedical knowledge by estimating pixel-wise stiffness online using a U-shaped feature extractor. The deformation field is then computed using polynomial regression by integrating the measured force applied by the US probe. Based on real-time estimation of pixel-by-pixel tissue properties, the learning-based approach enables the potential for anatomy-aware deformation correction. To demonstrate the effectiveness of the proposed DefCor-Net, images recorded at multiple locations on forearms and upper arms of six volunteers are used to train and validate DefCor-Net. The results demonstrate that DefCor-Net can significantly improve the accuracy of deformation correction to recover the original geometry (Dice Coefficient: from 14.3±20.9 to 82.6±12.1 when the force is 6N). Code: https://github.com/KarolineZhy/DefCorNet.
AB - The recovery of morphologically accurate anatomical images from deformed ones is challenging in ultrasound (US) image acquisition, but crucial to accurate and consistent diagnosis, particularly in the emerging field of computer-assisted diagnosis. This article presents a novel physics-aware deformation correction approach based on a coarse-to-fine, multi-scale deep neural network (DefCor-Net). To achieve pixel-wise performance, DefCor-Net incorporates biomedical knowledge by estimating pixel-wise stiffness online using a U-shaped feature extractor. The deformation field is then computed using polynomial regression by integrating the measured force applied by the US probe. Based on real-time estimation of pixel-by-pixel tissue properties, the learning-based approach enables the potential for anatomy-aware deformation correction. To demonstrate the effectiveness of the proposed DefCor-Net, images recorded at multiple locations on forearms and upper arms of six volunteers are used to train and validate DefCor-Net. The results demonstrate that DefCor-Net can significantly improve the accuracy of deformation correction to recover the original geometry (Dice Coefficient: from 14.3±20.9 to 82.6±12.1 when the force is 6N). Code: https://github.com/KarolineZhy/DefCorNet.
KW - AI for medicine
KW - Anatomy-aware ultrasound imaging
KW - Dense displacement field estimation
KW - Force sensing
KW - Medical image analysis
KW - Physics-aware ultrasound imaging
KW - Robotic ultrasound
KW - Stiffness estimation in ultrasound imaging
KW - Ultrasound elastography
KW - Ultrasound image analysis
KW - Ultrasound image deformation correction
UR - http://www.scopus.com/inward/record.url?scp=85171625623&partnerID=8YFLogxK
U2 - 10.1016/j.media.2023.102923
DO - 10.1016/j.media.2023.102923
M3 - Article
C2 - 37688982
AN - SCOPUS:85171625623
SN - 1361-8415
VL - 90
JO - Medical Image Analysis
JF - Medical Image Analysis
M1 - 102923
ER -