KEYWORDS: Computer simulations, Magnetic resonance imaging, Data modeling, Probability theory, Machine learning, 3D image processing, Neuroimaging, Human-computer interaction
Transcranial magnetic stimulation is a non-invasive therapeutic procedure in which specific cortical brain regions are stimulated in order to disrupt abnormal neural behaviour. This procedure requires the annotation of a number of cortical point targets which is often performed by a human expert. Nevertheless, there is a large degree of variability between experts that cannot be described readily using the existing zero-mean uni-modal error model common in computer-assisted interventions. This is due to the error between experts arising from a difference of type rather than a difference of degree, that is, experts are not necessarily picking the same point with some error, but are picking fundamentally different points. In order to model these types of localisation errors, this paper proposes a simple probabilistic model that uses the agreement between annotations as a basis, not requiring a ground-truth annotation to be strictly known. This work will allow for the localisation error in transcranial magnetic stimulation to be better described which may spur further developments in clinical training as well as machine learning for cortical point localisation.
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