Assessment of age-dependent sexual dimorphism in paediatric vertebral size and density using a statistical shape and statistical appearance modelling approach.

This work focuses on the growth patterns of the human fourth lumbar vertebra (L4) in a paediatric population, with specific attention to sexual dimorphism. The study aims to understand morphological and density changes in the vertebrae through age-dependent statistical shape and statistical appearance models, which can describe full three-dimensional anatomy. Results show that the main growth patterns are associated with isotropic volumetric vertebral growth, a decrease in the relative size of the vertebral foramen, and an increase in the length of the transverse processes.

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.

Assessment of thoracic spinal curvatures in static postures using spatially tracked 3D ultrasound volumes: a proof-of-concept study.

The assessment of spinal posture is a difficult endeavour given the lack of identifiable bony landmarks for placement of skin markers. Moreover, potentially significant soft tissue artefacts along the spine further affect the accuracy of marker-based approaches. The objective of this proof-of-concept study was to develop an experimental framework to assess spinal postures by using three-dimensional (3D) ultrasound (US) imaging.

Automatic deep learning-based consolidation/collapse classification in lung ultrasound images for COVID-19 induced pneumonia.

Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the identification of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets.