DefCor-Net: Physics-Aware Ultrasound Deformation Correction
Zhongliang Jiang Yue Zhou Dongliang Cao Nassir Navab
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 anatomy-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 $14.3\pm20.9$ to $82.6\pm12.1$ when
the force is $6N$).