Stereotactic surgical planning using three dimensional reconstruction and artificial neural networks

Recent research into different artificial neural network structures and topologies suggests the possibility of implementing a particular application. The goal is for the neural network to represent the input function in a natural manner. The authors describe such an implementation in the field of neurosurgical planning, where a set of neural networks represents the lesion to be treated as well as the different functional regions of the brain. It is shown that this neural network structure can actively and effectively assist in the surgical planning. Emphasis is on stereotactic radiosurgery, whereby a high dose of radiation is delivered to the lesion. This modality allows for extensive implementation of the neural network features in a natural way, using Gaussian potential functions for the neural activation. The goal of decreasing the procedural risk factor in stereotactic surgery is accomplished by implementing the visual interface and a framework of artificial neural networks