AI-assisted image segmentation and registration for spine navigation systems

Spine surgery often requires real-time X-ray images to guide surgical tools safely around delicate structures. However, using X-rays continuously during long operations can expose both patients and medical staff to unnecessary radiation. One way to reduce this risk is to use saved X-ray images for navigation rather than continuous exposure; however, this requires precise tool tracking to ensure accurate guidance. ClaroNav Kolahi Inc. (CKI) is a Canadian company that develops surgical navigation systems, including a spine navigation platform that can operate from a saved X-ray image. In this project, the intern will help design a method that uses small reference markers placed on the X-ray machine and visible to a camera-based tracking system, along with artificial intelligence–based image analysis to automatically link the position of surgical tools in real time with that saved image. This ensures navigation remains accurate even if the patient moves or if the image is rotated or scaled. For CKI, this innovation will make their navigation system more reliable and compatible with a wider range of hospitals, while also reducing radiation risks for patients and surgical teams.

Faculty Supervisor:

Parvin Mousavi

Student:

Partner:

ClaroNav Kolahi Inc

Discipline:

Computer science

Sector:

Manufacturing

University:

Queen's University

Program: