  Medical ultrasound technology is widely used in routine clinical applications
such as disease diagnosis and treatment as well as other applications like
real-time monitoring of human tongue shapes and motions as visual feedback in
second language training. Due to the low-contrast characteristic and noisy
nature of ultrasound images, it might require expertise for non-expert users to
recognize tongue gestures. Manual tongue segmentation is a cumbersome,
subjective, and error-prone task. Furthermore, it is not a feasible solution
for real-time applications. In the last few years, deep learning methods have
been used for delineating and tracking tongue dorsum. Deep convolutional neural
networks (DCNNs), which have shown to be successful in medical image analysis
tasks, are typically weak for the same task on different domains. In many
cases, DCNNs trained on data acquired with one ultrasound device, do not
perform well on data of varying ultrasound device or acquisition protocol.
Domain adaptation is an alternative solution for this difficulty by
transferring the weights from the model trained on a large annotated legacy
dataset to a new model for adapting on another different dataset using
fine-tuning. In this study, after conducting extensive experiments, we
addressed the problem of domain adaptation on small ultrasound datasets for
tongue contour extraction. We trained a U-net network comprises of an
encoder-decoder path from scratch, and then with several surrogate scenarios,
some parts of the trained network were fine-tuned on another dataset as the
domain-adapted networks. We repeat scenarios from target to source domains to
find a balance point for knowledge transfer from source to target and vice
versa. The performance of new fine-tuned networks was evaluated on the same
task with images from different domains.
