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Reinforcement learning for 3D surgery planning of Femoral Head Reduction Osteotomy (in collaboration with Balgrist hospital)
In the course of this master thesis, you will help us to improve our current surgery planning methods by developing an approach to predict the reposition of the fragment and the pose of the cutting planes defining the bone wedge.
Keywords: deep learning, reinforcement learning, optimization.
Morbus Legg-Calvé-Perthes is a paediatric disorder of the lower extremities, causing deformities of the femoral head. Surgical treatment for this bone deformity can be achieved by a procedure known as femoral head reduction osteotomy (FHRO), which involves the resection of a wedge from the femoral head to restore the function of the joint.
The preoperative planning of this procedure is a complex three-dimensional (3D) optimization problems involving more than 20 degrees of freedom (DoF) as it comprises the calculation of the surgical cuts and the reposition of the resected fragment to the desired anatomical position. This process is currently done manually in collaboration between engineers and surgeons.
Morbus Legg-Calvé-Perthes is a paediatric disorder of the lower extremities, causing deformities of the femoral head. Surgical treatment for this bone deformity can be achieved by a procedure known as femoral head reduction osteotomy (FHRO), which involves the resection of a wedge from the femoral head to restore the function of the joint.
The preoperative planning of this procedure is a complex three-dimensional (3D) optimization problems involving more than 20 degrees of freedom (DoF) as it comprises the calculation of the surgical cuts and the reposition of the resected fragment to the desired anatomical position. This process is currently done manually in collaboration between engineers and surgeons.
In the course of this master thesis, you will help us to improve our current surgery planning methods by developing an approach to predict the reposition of the fragment and the pose of the cutting planes defining the bone wedge. The objective of this master thesis is to apply deep (reinforcement) learning techniques to automatically find an optimal solution for the preoperative planning of FHRO.
We will start by solving a simplified version of the optimization problem, with a reduced DoF involving only the calculation of the bone fragment reposition and we will gradually increase the DoF and the complexity of the task. This project is part of a bigger framework, which is currently under development in our clinic for optimal surgical outcomes. (The student will mainly work at the Balgrist CAMPUS)
Requirements: Hands-on experience in reinforcement learning, deep learning. Strong coding skills in Python. Experience in mathematical optimization and spatial transformation is a plus.
In the course of this master thesis, you will help us to improve our current surgery planning methods by developing an approach to predict the reposition of the fragment and the pose of the cutting planes defining the bone wedge. The objective of this master thesis is to apply deep (reinforcement) learning techniques to automatically find an optimal solution for the preoperative planning of FHRO.
We will start by solving a simplified version of the optimization problem, with a reduced DoF involving only the calculation of the bone fragment reposition and we will gradually increase the DoF and the complexity of the task. This project is part of a bigger framework, which is currently under development in our clinic for optimal surgical outcomes. (The student will mainly work at the Balgrist CAMPUS)
Requirements: Hands-on experience in reinforcement learning, deep learning. Strong coding skills in Python. Experience in mathematical optimization and spatial transformation is a plus.
Dr. Oswald Martin (martin.oswald@inf.ethz.ch), Ackermann Joëlle (joelle.ackermann@balgrist.ch) Prof. Philipp Fuernstahl (philipp.fuernstahl@balgrist.ch)
Dr. Oswald Martin (martin.oswald@inf.ethz.ch), Ackermann Joëlle (joelle.ackermann@balgrist.ch) Prof. Philipp Fuernstahl (philipp.fuernstahl@balgrist.ch)