Automatic GAN-based MRI volume synthesis from US volumes: a proof of concept investigation.

Usually, a baseline image, either through magnetic resonance imaging (MRI) or computed tomography (CT), is captured as a reference before medical procedures such as respiratory interventions like Thoracentesis. In these procedures, ultrasound (US) imaging is often employed for guiding needle placement during Thoracentesis or providing image guidance in MISS procedures within the thoracic region. Following the procedure, a post-procedure image is acquired to monitor and evaluate the patient's progress.

Automatic deep learning-based pleural effusion segmentation in lung ultrasound images.

Background: Point-of-care lung ultrasound (LUS) allows real-time patient scanning to help diagnose pleural effusion (PE) and plan further investigation and treatment. LUS typically requires training and experience from the clinician to accurately interpret the images. To address this limitation, we previously demonstrated a deep-learning model capable of detecting the presence of PE on LUS at an accuracy greater than 90%, when compared to an experienced LUS operator.