Computer vision guided rapid and precise automated cranial microsurgeries in rodents

Abstract

Neuroscientists employ various experimental procedures to interface with the brain to study and perturb the neural activity during behavior. A common procedure that allows such physical interfacing is cranial microsurgery, wherein small to large craniotomies are performed in the overlying skull for insertion of neural interfaces or implantation of optically clear windows for long-term cranial observation. Performing craniotomies is, however, a skilled task that requires significant time and practice and further needs to be carried out precisely to ensure that the procedure does not cause damage to the underlying brain and dura. Here, we present a computer vision-guided craniotomy robot (CV-Craniobot) that utilizes machine learning to accurately estimate the dorsal skull anatomy from optical coherence tomography (OCT) images. Instantaneous information of the skull morphology is used by a robotic mill to rapidly and precisely remove the skull from a desired craniotomy location. We show that the CV-Craniobot can perform small (2 - 4 mm diameter) craniotomies with near 100% success rates within 2 minutes and large craniotomies encompassing most of the dorsal cortex in less than 5 minutes. Thus, the CV-Craniobot enables rapid and precise craniotomies, significantly reducing surgery time as compared to human practitioners and eliminating the need for long training.