Using artificial intelligence to quantify dynamic retraction of brain tissue and the manipulation of instruments in neurosurgery.

PURPOSE: There is no objective way to measure the amount of manipulation and retraction of neural tissue by the surgeon. Our goal is to develop metrics quantifying dynamic retraction and manipulation by instruments during neurosurgery.

METHODS

We trained a convolutional neural network (CNN) to analyze microscopic footage of neurosurgical procedures and thereby generate metrics evaluating the surgeon's dynamic retraction of brain tissue and, using an object tracking process, evaluate the surgeon's manipulation of the instruments themselves. U-Net image segmentation is used to output bounding polygons around cerebral parenchyma of interest, as well as the vascular structures and cranial nerves. A channel and spatial reliability tracker framework is used in conjunction with our CNN to track desired surgical instruments.

RESULTS

Our network achieved a state-of-the-art intersection over union ([Formula: see text]) for biological tissue segmentation. Multivariate statistical analysis was used to evaluate dynamic retraction, tissue handling, and instrument manipulation.

CONCLUSION

Our model enables to evaluate dynamic retraction of soft tissue and manipulation of instruments during a surgical procedure, while accounting for movement of the operative microscope. This model can potentially provide the surgeon with objective feedback about the movement of instruments and its effect on brain tissue.