  Ultrasound (US) images usually contain identifying information outside the
ultrasound fan area and manual annotations placed by the sonographers during
exams. For those images to be exploitable in a Deep Learning framework, one
needs to first delineate the border of the fan which delimits the ultrasound
fan area and then remove other annotations inside. We propose a parametric
probabilistic approach for the first task. We make use of this method to
generate a training data set with segmentation masks of the region of interest
(ROI) and train a U-Net to perform the same task in a supervised way, thus
considerably reducing computational time of the method, one hundred and sixty
times faster. These images are then processed with existing inpainting methods
to remove annotations present inside the fan area. To the best of our
knowledge, this is the first parametric approach to quickly detect the fan in
an ultrasound image without any other information than the image itself.
