Improved image guidance is needed for neurosurgeons to reduce the residual remaining clot levels during minimally invasive evacuation of intracerebral hemorrhage (ICH) while not causing rebleeds. Neurosurgeons would benefit from a means to periodically render the clot volume against surrounding normal tissue during mechanical evacuation or pharmaceutical-based clot-busting. Using convolutional neural networks (CNN), we created machine learning models to automatically segment the constituent clot and edema induced by ICH cases using T2-weighted MR images. The CCN’s output results were found to be in agreement with manual segmentations of the same ICH cases.
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