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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.
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.
Yunlong Song (song@ifi.uzh.ch), Ackermann Joelle (joelle.ackermann@balgrist.ch) Prof. Philipp Fuernstahl (philipp.fuernstahl@balgrist.ch)
Yunlong Song (song@ifi.uzh.ch), Ackermann Joelle (joelle.ackermann@balgrist.ch) Prof. Philipp Fuernstahl (philipp.fuernstahl@balgrist.ch)