Introducing Pareto-based MOEA to Neurosurgery Preoperative Path Planning

This paper presents the first implementation of NSGA-II in neurosurgery preoperative path planning. Deep Brain Stimulation (DBS) is a surgical treatment of Parkinson's disease that can be regarded as a multi-objective optimization problem, searching for the best compromise between multiple electrode placement rules. Most of the current automatic decision-making processes use aggregative approaches with single objective optimization, even though they are known for their inability to find all Pareto-optimal solutions. Firstly, we show this is the case on 20 datasets of patients by comparing our implementation of NSGA-II to the weighted sum (WS) strategy. Then, we show that it requires about 9 hours to find equivalent results using a deterministic scan of the search space where NSGA-II does it in about 3mn. This paper presents an objective validation that even simple techniques such as NSGA-II should be used by surgeons over more intuitive weighted based methods.

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