Bipixel rotation method: An algorithm for chirality analysis of biological mesh network

Vertebrates are bilateral organisms characterized by both symmetrical and asymmetrical structures. While skeletons are grossly symmetrical, microscopic biological networks within the bone-such as cortical canals containing vascular networks-often exhibit deviations from precise mirror-image symmetry. These deviations make it challenging to detect subtle chiral biases amidst random structural variations. We developed the "bipixel rotation method" to quantify the extent of these biases, thereby enabling the characterization of porcine femoral cortical canals. In this approach, binarized 2D images are rotated stepwise and skeletonized, followed by the quantification of vertically adjacent pixel pairs. We defined "center angle" as the rotation angle that yields the peak vertical bipixel count, and calculated "down-right bias" based on the asymmetry of the distribution of vertical bipixels around this center angle. After validating the method using alphabetical fonts and geometric wallpaper patterns, we applied the method to the cortical canal layers in porcine fibrolamellar bone visualized via nano-computed tomography (nano-CT). The method successfully detected potential chirality in the cortical canals, identifying up-right versus down-right biases, demonstrating its capability to capture even subtle structural chirality. These results suggest that the bipixel method is a versatile tool for chirality analysis of diverse biological mesh structures in the body.