RESUMO
This study presents a computational tool for auto-segmenting the distribution of brain infusions observed by magnetic resonance imaging. Clinical usage of direct infusion is increasing as physicians recognize the need to attain high drug concentrations in the target structure with minimal off-target exposure. By co-infusing a Gadolinium-based contrast agent and visualizing the distribution using real-time using magnetic resonance imaging, physicians can make informed decisions about when to stop or adjust the infusion. However, manual segmentation of the images is tedious and affected by subjective preferences for window levels, image interpolation and personal biases about where to delineate the edge of the sloped shoulder of the infusion. This study presents a computational technique that uses a Gaussian Mixture Model to efficiently classify pixels as belonging to either the high-intensity infusate or low-intensity background. The algorithm was implemented as a distributable plug-in for the widely used imaging platform OsiriX®. Four independent operators segmented fourteen anonymized datasets to validate the tool's performance. The datasets were intra-operative magnetic resonance images of infusions into the thalamus or putamen of non-human primates. The tool effectively reproduced the manual segmentation volumes, while significantly reducing intra-operator variability by 67±18%. The tool will be used to increase efficiency and reduce variability in upcoming clinical trials in neuro-oncology and gene therapy.