Intraoperative Stereovision Cortical Surface Segmentation Using Fast Segment Anything Model

Introduction In image-guided open cranial surgeries, brain deformation may compromise the accuracy of image guidance immediately following the opening of the dura. A biomechanical model has been developed to update pre-operative MR images to match intraoperative stereovision (iSV), and maintain the accuracy of image guidance. Current methods necessitate manual segmentation of the cortical surface from iSV, a process that demands expertise and prolongs computational time. Methods In this study, we adopted the Fast Segment Anything Model (FastSAM), a newly developed deep learning model that automatically can segment the cortical surface from iSV after dural opening without customized training. We evaluated its performance against manual segmentation as well as a U-Net model. In one patient case, FastSAM was applied to segment the cortical surface with an automatic box prompt, and the segmentation was used for image updating. We compared the three cortical surface segmentation methods in terms of segmentation accuracy (Dice Similarity Coefficient; DSC) and image updating accuracy (target registration errors; TRE). Results All three segmentation methods demonstrated high DSC (>0.95). FastSAM and manual segmentation produced similar performance in terms of image updating efficiency and TRE (similar to 2.2 mm). Conclusion In summary, the performance of FastSAM was consistent with manual segmentation in terms of segmentation accuracy and image updating accuracy. The results suggest FastSAM can be employed in the image updating process to replace manual segmentation to improve efficiency and reduce user dependency.