Rethinking Ultrasound Augmentation: A Physics-Inspired Approach [article]

Maria Tirindelli, Christine Eilers, Walter Simson, Magdalini Paschali, Mohammad Farid Azampour, Nassir Navab
2021 arXiv   pre-print
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
more » ... alistic 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.
arXiv:2105.02188v1 fatcat:v4f4uoomavbgrnuq5mwnoktlmi