Knee segmentation

Musculoskeletal disorders are a common cause of pain and disability affecting millions of people around the world. The Orthopaedic Research Laboratory (ORL) of the Radboudumc performs research that is focused on congenital deformities of the knee. Within this spectrum patellofemoral instability due to trochlear deformity and tubercle mal-positions are common. The instability causes patellar dislocations, which is the most common acute knee disorder in children and adolescents, with negative effect on quality of life.

In an ongoing research project, musculoskeletal dynamic models based on real time dynamic CT scans are used to optimize pre-operative surgical planning for patellofemoral surgery. The goal is to optimize stability and minimize patellofemoral contact forces. With the aid of surgical navigation tools and cutting guides the pre-operative planning can be executed with high accuracy. This will hopefully lead to more satisfied patients, with less morbidities such as osteoarthritis along the way.

The RTC Deep Learning is developing a web-based application for the automatic segmentation of the patella, fibula and tibia in the dynamic CT scans of the knee.

People

Sebastiaan van de Groes

Sebastiaan van de Groes

Orthopedic surgeon

Orthopedics, Radboudumc

Hans Dunning

Hans Dunning

PhD student

Orthopedics, Radboudumc

Silvan Quax

Silvan Quax

RTC Deep Learning

Ajay Patel

Ajay Patel

Coordinator RTC Deep Learning

Bram van Ginneken

Bram van Ginneken

Professor

Diagnostic Image Analysis Group

James Meakin

James Meakin

Lead Research Software Engineer

DIAG Research Software Engineering

Stan Buckens

Stan Buckens

Radiologist

Radiology, Radboudumc