Ultrasound Image Quality Control Using Speech-Assisted Switchable CycleGAN

Unlike computed tomography (CT) and magnetic resonance imaging (MRI) in which the image quality (IQ) is controlled by predefined acquisition setups, the IQ of ultrasound (US) is heavily dependent upon operators. In particular, an operator often adjusts the system parameters in a real-time manner based on his/her preference. Unfortunately, there are many cases where such real-time control of IQ is difficult, especially in the intensive care unit (ICU) or operating room (OR), since the operator should simultaneously treat patients in sterile status and adjust the system parameters. To address this, inspired by the recent success of Switchable CycleGAN using Adaptive Instance Normalization (AdaIN) layers, here we propose a novel speech-assisted Switchable CycleGAN architecture that can be controlled by operator's verbal commands. Specifically, we employ a Speech Recognition Module (SRM) to generate AdaIN codes for Switchable CycleGAN. In particular, our SRM is based on the pre-trained model so that it can be less effected by the operator's voice and robustly generate AdaIN codes. Various experimental results confirm that the proposed method can successfully control IQ of US by speech.