Quality assessment of transperineal ultrasound images of the male pelvic region using deep learning

Abstract

Ultrasound imaging is one of the image modalities that can be used for radiation dose guidance during radiotherapy workflows of prostate cancer patients. To allow for image acquisition during the treatment, the ultrasound probe needs to be positioned on the body of the patient before the radiation delivery starts using e.g. a mechanical arm. This is an essential step, as the operator cannot be present in the room when the radiation beam is turned on. Changes in anatomical structures or small motions of the patient during the dose delivery can compromise ultrasound image quality, due to e.g. loss of acoustic coupling or sudden appearance of shadowing artifacts. Currently, an operator is still needed to identify this quality loss. We introduce a prototype deep learning algorithm that can automatically assign a quality score to 2D US images of the male pelvic region based on their usability during an ultrasound guided radiotherapy workflow. It has been shown that the performance of this algorithm is comparable with a medical accredited sonographer and two radiation oncologists.