Privileged Anatomical and Protocol Discrimination in Trackerless 3D
  Ultrasound Reconstruction
Qi Li Ziyi Shen Qian Li Dean C. Barratt Thomas Dowrick Matthew J. Clarkson Tom Vercauteren Yipeng Hu
Three-dimensional (3D) freehand ultrasound (US) reconstruction without using
any additional external tracking device has seen recent advances with deep
neural networks (DNNs). In this paper, we first investigated two identified
contributing factors of the learned inter-frame correlation that enable the
DNN-based reconstruction: anatomy and protocol. We propose to incorporate the
ability to represent these two factors - readily available during training - as
the privileged information to improve existing DNN-based methods. This is
implemented in a new multi-task method, where the anatomical and protocol
discrimination are used as auxiliary tasks. We further develop a differentiable
network architecture to optimise the branching location of these auxiliary
tasks, which controls the ratio between shared and task-specific network
parameters, for maximising the benefits from the two auxiliary tasks.
Experimental results, on a dataset with 38 forearms of 19 volunteers acquired
with 6 different scanning protocols, show that 1) both anatomical and protocol
variances are enabling factors for DNN-based US reconstruction; 2) learning how
to discriminate different subjects (anatomical variance) and predefined types
of scanning paths (protocol variance) both significantly improve frame
prediction accuracy, volume reconstruction overlap, accumulated tracking error
and final drift, using the proposed algorithm.