Automatic positioning of cutting planes for bone tumor resection surgery.

In bone tumor resection surgery, patient-specific cutting guides aid the surgeon in the resection of a precise part of the bone. Despite the use of automation methodologies in surgical guide modeling, to date, the placement of cutting planes is a manual task. This work presents an algorithm for the automatic positioning of cutting planes to reduce healthy bone resected and thus improve post-operative outcomes. The algorithm uses particle swarm optimization to search for the optimal positioning of points defining a cutting surface composed of planes parallel to a surgical approach direction. The quality of a cutting surface is evaluated by an objective function that considers two key variables: the volumes of healthy bone resected and tumor removed. The algorithm was tested on three tumor cases in long bone epiphyses (two tibial, one humeral) with varying plane numbers. Optimal optimization parameters were determined, with varying parameters through iterations providing lower mean and standard deviation of the objective function. Initializing particle swarm optimization with a plausible cutting surface configuration further improved stability and minimized healthy bone resection. Future work is required to reach 3D optimization of the planes positioning, further improving the solution.