Nondestructive automatic detection of bregma and lambda points in rodent skull anatomy images

based on an open-source image set, this study calculated the relative positional relationships between the eyes and the bregma and lambda points, using the nose tip as a reference point. We found that the correlation coefficients were as high as 0.942 and 0.935, respectively. Leveraging these findings, we fitted linear regression equations that can estimate the locations of the bregma and lambda points based on eye positions, with theoretical errors of 0.35mm and 0.57mm, respectively. Additionally, an automatic recognition algorithm for the nose tip and eyes was designed using openCV and YOLOV5, enabling automatic localization of the bregma and lambda points. Upon validation, the actual recognition errors of this method were 0.41mm and 0.66mm, respectively, demonstrating high precision. This noninvasive and straightforward automatic recognition approach not only effectively reduces surgical trauma to animals but also avoids the precision errors associated with manual positioning, offering new possibilities for the clinical application of brain-computer interface technology.