About Project OrthoVis 2.0
OrthoVis 2.0 is an initiative to modernise and enhance a specialised software tool used for assessing joint motion (kinematics) through dynamic fluoroscopy (video X-ray). Originally developed over 15 years ago in MATLAB, the current version of OrthoVis, while highly accurate, is too slow for practical clinical use—taking hours to process just a few seconds of imaging data. Additionally, its segmentation process still requires significant manual input, further delaying results.
This project aims to leverage modern advancements in computer vision and machine learning to drastically improve processing speed, automate segmentation, and enhance usability for clinicians and researchers. By transitioning the software to a more efficient and scalable architecture, the team will develop expertise in image processing, software development, and AI-driven medical applications.
Team members aim to gain hands-on experience in programming (C++, Python, Java), machine learning, and medical imaging. Working closely with clinical and engineering experts, students will contribute to open-source medical software that improves patient care and research outcomes.
OrthoVis 2.0 Student Team
Australian National University - 2025
Our team is made up of seven enthusiastic members from differing academic backgrounds, with unique skillsets, and interests. We are all excited to be working on a project that gives us an opportunity to apply our technical skills in a practical application in OrthoVis 2.0.
Project Resources
Please click below to navigate to our project GitHub repository. Here you can find all of the code that we have been working on, as well as important project documentation. Our documentation also incudes various links to papers that explore the fundamental concepts that underpin the original version of OrthoVis.
Work Hours
Throughout 2025, we will be working in The Hive, located in Building 108 at the ANU between the hours of 8AM and 8PM on Wednesday, every week. Please come find us if you want to have a chat and learn more about the project!