The aim of this study is to determine if machine learning can be used to automatically highlight key anatomy on the ultrasound image to help anaesthetists perform ultrasound-guided regional anaesthesia.

The study will involve adult volunteers who are willing to be scanned by a trained sonographer to collect ultrasound video data for the following categories:

• Adductor canal
• Popliteal fossa
• Fascia Iliaca
• Rectus sheath
• Axillary
• ESP
• PECS
• Interscalene

Each volunteer will be scanned to collect data for every category in the list. Where applicable, both sides of the body will be scanned.

The videos will be segmented by hand to identify the relevant anatomical regions for each category.

The primary objective for this study is to provide the range of data required to develop robust models in conjunction with additional data from patients undergoing a Peripheral Nerve Block procedure that are able to produce the desired segmentation on the unseen validation images. The models will be scored using the standard "Mean intersection over Union" pixel-level metric for semantic segmentation.

Inclusion Criteria:

1. Male or female, at least 18 years of age;

2. Willing to undergo ultrasound scanning and provide ultrasound video data for the following categories:

   • Adductor canal
   • Popliteal fossa
   • Fascia Iliaca
   • Rectus sheath
   • Axillary
   • ESP
   • PECS
   • Interscalene

3. Able to comprehend and sign the Informed Consent prior to enrolment in the study.

Exclusion Criteria:

1. Aged <18 years of age;
2. Unwilling or unable to provide informed consent.

Healthy volunteers