  Medical Ultrasound (US), despite its wide use, is characterized by artifacts
and operator dependency. Those attributes hinder the gathering and utilization
of US datasets for the training of Deep Neural Networks used for
Computer-Assisted Intervention Systems. Data augmentation is commonly used to
enhance model generalization and performance. However, common data augmentation
techniques, such as affine transformations do not align with the physics of US
and, when used carelessly can lead to unrealistic US images. To this end, we
propose a set of physics-inspired transformations, including deformation,
reverb and Signal-to-Noise Ratio, that we apply on US B-mode images for data
augmentation. We evaluate our method on a new spine US dataset for the tasks of
bone segmentation and classification.
