Computer Vision Methods to Characterize the Morphology of Mouse Skulls for Neuroscience Applications

Abstract

Computer vision is a powerful tool for automating the characterization of biological specimen morphology. Classical morphometric studies have provided crucial insights into the skull anatomy of commonly used wildtype (WT) laboratory mice strains such as the C57BL/6. With the increasing use of transgenic (TG) animals in neuroscience research, it is important to determine whether the results from morphometric studies performed on WT strains can be extended to TG strains derived from these WT strains. In this thesis, we first report a new computer vision-based analysis pipeline for surveying mouse skull morphology using Microcomputed Tomography (µCT) scans. We applied this pipeline to study and compare eight cohorts of adult mice from two strains, including both male and female mice at two age points. We found that the overall skull morphology was generally conserved between cohorts, with only 13% of landmark distance differences reaching statistical significance. In addition, we surveyed the dorsal skull bone thickness differences between cohorts. We observed significantly thicker dorsal, parietal, and/or interparietal bones in WT, male, or older mice for 53% of thickness comparisons. Many neuroscience experiments require penetrating the mouse skull to record or modulate neural activity in the brain. Craniotomy procedures on sub-millimeter thick skull tissue are time-consuming to perform manually and require substantial training to attain an acceptable success rate. Previous researchers have used automation to reduce the training needed, increase speed, and minimize variability, but insufficient knowledge of the dorsal skull thickness limits their performance. We thus present a fast, non-invasive method which employs preoperative Optical Coherence Tomography (OCT) imaging to guide a robot to perform single-pass craniotomies in mice. The mouse skull is scanned with an OCT scanner immediately prior to surgery, then a custom computer vision-based analysis pipeline extracts an approximately 10 µm axial by 20 µm lateral resolution 3D profile of the dorsal and ventral surfaces of the mouse calvaria within the Field of View (FOV). A cutting path is generated based on the depth of the ventral surface along the desired craniotomy path. Comparison with µCT skull thickness data and preliminary surgery results indicates that this method provides an acceptable profile across most of the mouse dorsal skull, though more iteration is required to ensure accurate measurement of the area around the lambdoid sinus.